{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "Parameter Heatmap\n",
    "==========\n",
    "\n",
    "This tutorial will show how to optimize strategies with multiple parameters and how to examine and reason about optimization results.\n",
    "It is assumed you're already familiar with\n",
    "[basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html).\n",
    "\n",
    "First, let's again import our helper moving average function.\n",
    "In practice, one should use functions from an indicator library, such as\n",
    "[TA-Lib](https://github.com/mrjbq7/ta-lib) or\n",
    "[Tulipy](https://tulipindicators.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
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       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
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       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
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       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
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       "\n",
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       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
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       "      return\n",
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       "\n",
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       "\n",
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       "      var bk_div = document.createElement(\"div\");\n",
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       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
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       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
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       "\n",
       "\n",
       "  function run_callbacks() {\n",
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       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
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       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\": \"qkRvDQVAIfzsJo40iRBbxt6sttt0hv4lh74DG7OK4MCHv4C5oohXYoHUM5W11uqS\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\": \"Sb7Mr06a9TNlet/GEBeKaf5xH3eb6AlCzwjtU82wNPyDrnfoiVl26qnvlKjmcAd+\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\": \"HaJ15vgfmcfRtB4c4YBOI4f1MUujukqInOWVqZJZZGK7Q+ivud0OKGSTn/Vm2iso\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\": \"qkRvDQVAIfzsJo40iRBbxt6sttt0hv4lh74DG7OK4MCHv4C5oohXYoHUM5W11uqS\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\": \"Sb7Mr06a9TNlet/GEBeKaf5xH3eb6AlCzwjtU82wNPyDrnfoiVl26qnvlKjmcAd+\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\": \"HaJ15vgfmcfRtB4c4YBOI4f1MUujukqInOWVqZJZZGK7Q+ivud0OKGSTn/Vm2iso\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from backtesting.test import SMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our strategy will be a similar moving average cross-over strategy to the one in\n",
    "[Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html),\n",
    "but we will use four moving averages in total:\n",
    "two moving averages whose relationship determines a general trend\n",
    "(we only trade long when the shorter MA is above the longer one, and vice versa),\n",
    "and two moving averages whose cross-over with daily _close_ prices determine the signal to enter or exit the position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backtesting import Strategy\n",
    "from backtesting.lib import crossover\n",
    "\n",
    "\n",
    "class Sma4Cross(Strategy):\n",
    "    n1 = 50\n",
    "    n2 = 100\n",
    "    n_enter = 20\n",
    "    n_exit = 10\n",
    "    \n",
    "    def init(self):\n",
    "        self.sma1 = self.I(SMA, self.data.Close, self.n1)\n",
    "        self.sma2 = self.I(SMA, self.data.Close, self.n2)\n",
    "        self.sma_enter = self.I(SMA, self.data.Close, self.n_enter)\n",
    "        self.sma_exit = self.I(SMA, self.data.Close, self.n_exit)\n",
    "        \n",
    "    def next(self):\n",
    "        \n",
    "        if not self.position:\n",
    "            \n",
    "            # On upwards trend, if price closes above\n",
    "            # \"entry\" MA, go long\n",
    "            \n",
    "            # Here, even though the operands are arrays, this\n",
    "            # works by implicitly comparing the two last values\n",
    "            if self.sma1 > self.sma2:\n",
    "                if crossover(self.data.Close, self.sma_enter):\n",
    "                    self.buy()\n",
    "                    \n",
    "            # On downwards trend, if price closes below\n",
    "            # \"entry\" MA, go short\n",
    "            \n",
    "            else:\n",
    "                if crossover(self.sma_enter, self.data.Close):\n",
    "                    self.sell()\n",
    "        \n",
    "        # But if we already hold a position and the price\n",
    "        # closes back below (above) \"exit\" MA, close the position\n",
    "        \n",
    "        else:\n",
    "            if (self.position.is_long and\n",
    "                crossover(self.sma_exit, self.data.Close)\n",
    "                or\n",
    "                self.position.is_short and\n",
    "                crossover(self.data.Close, self.sma_exit)):\n",
    "                \n",
    "                self.position.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not a robust strategy, but we can optimize it.\n",
    "\n",
    "[Grid search](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search)\n",
    "is an exhaustive search through a set of specified sets of values of hyperparameters. One evaluates the performance for each set of parameters and finally selects the combination that performs best.\n",
    "\n",
    "Let's optimize our strategy on Google stock data using _randomized_ grid search over the parameter space, evaluating at most (approximately) 200 randomly chosen combinations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=9.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 322 ms, sys: 42 ms, total: 364 ms\n",
      "Wall time: 6.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "from backtesting import Backtest\n",
    "from backtesting.test import GOOG\n",
    "\n",
    "\n",
    "backtest = Backtest(GOOG, Sma4Cross, commission=.002)\n",
    "\n",
    "stats, heatmap = backtest.optimize(\n",
    "    n1=range(10, 110, 10),\n",
    "    n2=range(20, 210, 20),\n",
    "    n_enter=range(15, 35, 5),\n",
    "    n_exit=range(10, 25, 5),\n",
    "    constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,\n",
    "    maximize='Equity Final [$]',\n",
    "    max_tries=200,\n",
    "    random_state=0,\n",
    "    return_heatmap=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice `return_heatmap=True` parameter passed to\n",
    "[`Backtest.optimize()`](https://kernc.github.io/backtesting.py/doc/backtesting/backtesting.html#backtesting.backtesting.Backtest.optimize).\n",
    "It makes the function return a heatmap series along with the usual stats of the best run.\n",
    "`heatmap` is a pandas Series indexed with a MultiIndex, a cartesian product of all permissible (tried) parameter values.\n",
    "The series values are from the `maximize=` argument we provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1   n2   n_enter  n_exit\n",
       "30   40   15       10       10611.01\n",
       "          25       20        9006.74\n",
       "     60   15       10       11297.19\n",
       "          20       10       11539.78\n",
       "     80   25       10       11217.25\n",
       "                              ...   \n",
       "100  180  20       10       10711.02\n",
       "     200  25       10        8991.55\n",
       "                   15        9549.19\n",
       "                   20        8241.72\n",
       "          30       15        9701.12\n",
       "Name: Equity Final [$], Length: 205, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heatmap contains the results of all the runs,\n",
    "making it very easy to obtain parameter combinations for e.g. three best runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1   n2   n_enter  n_exit\n",
       "40   60   20       15       20019.78\n",
       "100  120  20       15       20545.03\n",
       "40   60   25       15       21014.63\n",
       "Name: Equity Final [$], dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap.sort_values().iloc[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we use vision to make judgements on larger data sets much faster.\n",
    "Let's plot the whole heatmap by projecting it on two chosen dimensions.\n",
    "Say we're mostly interested in how parameters `n1` and `n2`, on average, affect the outcome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>n2</th>\n",
       "      <th>40</th>\n",
       "      <th>60</th>\n",
       "      <th>80</th>\n",
       "      <th>100</th>\n",
       "      <th>120</th>\n",
       "      <th>140</th>\n",
       "      <th>160</th>\n",
       "      <th>180</th>\n",
       "      <th>200</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>9808.88</td>\n",
       "      <td>11418.49</td>\n",
       "      <td>12183.57</td>\n",
       "      <td>10970.01</td>\n",
       "      <td>9823.28</td>\n",
       "      <td>10634.02</td>\n",
       "      <td>12761.29</td>\n",
       "      <td>11129.58</td>\n",
       "      <td>10818.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>nan</td>\n",
       "      <td>15237.61</td>\n",
       "      <td>11123.54</td>\n",
       "      <td>8036.80</td>\n",
       "      <td>12899.93</td>\n",
       "      <td>12606.21</td>\n",
       "      <td>12108.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>9707.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>nan</td>\n",
       "      <td>6614.39</td>\n",
       "      <td>10249.40</td>\n",
       "      <td>10759.11</td>\n",
       "      <td>10741.92</td>\n",
       "      <td>11369.56</td>\n",
       "      <td>10508.96</td>\n",
       "      <td>11118.57</td>\n",
       "      <td>10282.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>6949.90</td>\n",
       "      <td>11278.54</td>\n",
       "      <td>9855.09</td>\n",
       "      <td>11674.47</td>\n",
       "      <td>12361.64</td>\n",
       "      <td>9277.57</td>\n",
       "      <td>9520.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>13241.52</td>\n",
       "      <td>8630.84</td>\n",
       "      <td>9447.04</td>\n",
       "      <td>9647.08</td>\n",
       "      <td>10854.71</td>\n",
       "      <td>9298.65</td>\n",
       "      <td>8120.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8312.86</td>\n",
       "      <td>9165.66</td>\n",
       "      <td>10169.51</td>\n",
       "      <td>11992.31</td>\n",
       "      <td>10748.77</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>11454.72</td>\n",
       "      <td>12570.32</td>\n",
       "      <td>10656.17</td>\n",
       "      <td>12041.58</td>\n",
       "      <td>10927.90</td>\n",
       "      <td>9396.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>12372.87</td>\n",
       "      <td>10093.88</td>\n",
       "      <td>11733.34</td>\n",
       "      <td>10711.02</td>\n",
       "      <td>9120.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "n2      40       60       80       100      120      140      160      180  \\\n",
       "n1                                                                           \n",
       "30  9808.88 11418.49 12183.57 10970.01  9823.28 10634.02 12761.29 11129.58   \n",
       "40      nan 15237.61 11123.54  8036.80 12899.93 12606.21 12108.13      nan   \n",
       "50      nan  6614.39 10249.40 10759.11 10741.92 11369.56 10508.96 11118.57   \n",
       "60      nan      nan  6949.90 11278.54  9855.09 11674.47 12361.64  9277.57   \n",
       "70      nan      nan 13241.52  8630.84  9447.04  9647.08 10854.71  9298.65   \n",
       "80      nan      nan      nan  8312.86  9165.66 10169.51 11992.31 10748.77   \n",
       "90      nan      nan      nan 11454.72 12570.32 10656.17 12041.58 10927.90   \n",
       "100     nan      nan      nan      nan 12372.87 10093.88 11733.34 10711.02   \n",
       "\n",
       "n2       200  \n",
       "n1            \n",
       "30  10818.95  \n",
       "40   9707.38  \n",
       "50  10282.21  \n",
       "60   9520.61  \n",
       "70   8120.51  \n",
       "80       nan  \n",
       "90   9396.30  \n",
       "100  9120.90  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hm = heatmap.groupby(['n1', 'n2']).mean().unstack()\n",
    "hm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot this table using the excellent [_Seaborn_](https://seaborn.pydata.org) package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='n2', ylabel='n1'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "sns.heatmap(hm[::-1], cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that, on average, we obtain the highest result using trend-determining parameters `n1=40` and `n2=60`,\n",
    "and it's not like other nearby combinations work similarly well — in our particular strategy, this combination really stands out.\n",
    "\n",
    "Since our strategy contains several parameters, we might be interested in other relationships between their values.\n",
    "We can use\n",
    "[`backtesting.lib.plot_heatmaps()`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.plot_heatmaps)\n",
    "function to plot interactive heatmaps of all parameter combinations simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"841efc58-5cec-4744-9269-0cfa6253a687\" data-root-id=\"1252\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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       "  var render_items = [{\"docid\":\"c468d9bb-e7c5-4dcb-873b-1bd0d8397e1e\",\"root_ids\":[\"1252\"],\"roots\":{\"1252\":\"841efc58-5cec-4744-9269-0cfa6253a687\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
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      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1252"
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     },
     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "Column(id='1252', ...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from backtesting.lib import plot_heatmaps\n",
    "\n",
    "\n",
    "plot_heatmaps(heatmap, agg='mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-based optimization\n",
    "\n",
    "Above, we used _randomized grid search_ optimization method. Any kind of grid search, however, might be computationally expensive for large data sets. In the follwing example, we will use\n",
    "[_scikit-optimize_](https://scikit-optimize.github.io)\n",
    "package to guide our optimization better informed using forests of decision trees.\n",
    "The hyperparameter model is sequentially improved by evaluating the expensive function (the backtest) at the next best point, thereby hopefully converging to a set of optimal parameters with as few evaluations as possible.\n",
    "\n",
    "So, with `method=\"skopt\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "! pip install scikit-optimize  # This is a run-time dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24.3 s, sys: 50.4 ms, total: 24.4 s\n",
      "Wall time: 24.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "stats_skopt, heatmap, optimize_result = backtest.optimize(\n",
    "    n1=[10, 100],      # Note: For method=\"skopt\", we\n",
    "    n2=[20, 200],      # only need interval end-points\n",
    "    n_enter=[10, 40],\n",
    "    n_exit=[10, 30],\n",
    "    constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,\n",
    "    maximize='Equity Final [$]',\n",
    "    method='skopt',\n",
    "    max_tries=200,\n",
    "    random_state=0,\n",
    "    return_heatmap=True,\n",
    "    return_optimization=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1  n2   n_enter  n_exit\n",
       "68  96   29       24       28424.02\n",
       "35  98   28       24       28658.80\n",
       "44  134  39       27       30251.81\n",
       "Name: Equity Final [$], dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap.sort_values().iloc[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the optimization runs somewhat slower even though `max_tries=` is the same. But that's due to the sequential nature of the algorithm and should actually perform rather comparably even in cases of _much larger parameter spaces_ where grid search would effectively blow up, but likely (hopefully) reaching a better local optimum than a randomized search would.\n",
    "A note of warning, again, to take steps to avoid\n",
    "[overfitting](https://en.wikipedia.org/wiki/Overfitting)\n",
    "insofar as possible.\n",
    "\n",
    "Understanding the impact of each parameter on the computed objective function is easy in two dimensions, but as the number of dimensions grows, partial dependency plots are increasingly useful.\n",
    "[Plotting tools from _scikit-optimize_](https://scikit-optimize.github.io/stable/modules/plots.html)\n",
    "take care of many of the more mundane things needed to make good and informative plots of the parameter space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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OJ8H0oOsmYIKqdlTVDqraXlWtNocxxpissAArytkHDGCP7u341bNz2Loti/sAjx6dvbZbIBFpC3yL5DWtVqrqxznqkjHGmBbOAqworUpLuH7CUL78aguT3/g8exfq0yd7bbdAqlqrql1VNdnmzTUi8qiInB5T3d0YY4wJnAVYMQ7YvYpj9u7FHa99ypJ1dWF3xwSnA06NrMNxqrsfBxwbao+MMcYUrRa7VU4yPz9mL16et5Ib/vMxd55p03nFwK3mbowxxuSEjWDF0adTG358yO5Mnb2C/326JvUb/Dr//ODbNEmJyJ4i8rKIzHafDxeRX4TdL2OMMcXJAqwEzv/mrvTvUsl1T89hW1NzsI1bJfcw3ANcA2wDUNWZOIVJjTHGmMBZgJVARatS/t+xQ/h01Wbuf3tRsI3bKsIwVKrq+zHHcrQ3kjHGmJbGAqwkxu/VnUMGdeNPL33Cqk1bg2t4+vTg2jJerRGR3QAFEJGTgeXJ32KMMcakxwKsJESE644bSkNjM7c8Pz/s7pjM/BhnL8LBIrIUuAy4MNQeGWOMKVoWYKUwsKot3/1Gf56cvpQVGwIaxerVK5h2jGeq+rmqHgZ0Awar6kGqGsxGlsYYY0wMC7A8OO+ggTSrcl9QuVjLlgXTjvFMRLqKyG3Am8BrIvJnEekadr+MMcYUJwuwPOjXpZIjh/XkofcWU1sfQF70pEmZt2H8egRYDZwEnOx+/WioPTLGGFO0LMDy6Adjd2Xj1kYeq/ky88auvz7zNoxfvVT116q60H38BugRdqeMMcYUJwuwPNqnf2dG79KZv/1vIU3NGnZ3jH8viMhpIlLiPr4DPB92p4wxxhQnC7B8OH/sQL78agvPz1kRdleMf+cDDwENQD3OlOEFIrJJRDaG2jNjjDFFxwIsH741pCe7dK3knjc/z6yhmppgOmQ8U9X2qlqiqmWq2sr9ur376BB2/4wxxhQXC7B8KC0Rzj1wIB9+sZ5pi78KuzvGB3GcKSK/dJ/3E5H9wu6XMcaY4mQBlk+nVPelY5tW3PvmwvQbqa4OrkMGEekkIk+IyDwR+VhE9o9z2h3A/sB33eebgb/mrJPGGGNaFAuwfKpsXcYZ3+jP83NW8MXaurC7Yxx/Bp5T1cHACODjOOd8Q1V/DGwFUNV1QOvcddEYY0xLYgFWGs4+YAClJcLf/pfBKJYJhIh0BL4JTAFQ1QZVXR/n1G0iUsrXexF2A5pz1U9jjDEtiwVYaejRoYLjRvTmsZov2VC3zX8D110XfKdaroE4RUP/LiIfisi9ItI2znm3AU8B3UXkBuAt4Lc57KcxxpgWpOACLBGZKCI1IlKzevXq0Prxg4N2pa6hiQffT2M7O6vk7kdV5PftPibGvF4G7APcqaqjgFrgZ7GNqOqDwFXA74DlwAmq+niW+26MMaaFEtXCLZpZXV2tNSGWPDjz3vf4ZNUm3rxqHK3LfMSqvXvbfoQeicg0VU24KkBEegLvquoA9/lY4Geqeoz7vEuy9lXVloMaY4wJXMGNYOWTH4wdyMqN9Tzzkc9gafny7HSoSDQ2NTNzyXqmvJU6x01VVwBfisgg99B4YG7UKdOAGvff1cAC4BP362lB9tsYY4yJKAu7A4Xs4D27sWePdtz71kJO3KcPIhJ2lwrS5vpGPvxiHTWL1lGz+Cs+/GI9dQ1Nfpq4GHhQRFoDnwPnRF5Q1YEAInIP8JSq/td9fhRwQlDfgzHGGBPNAqwMiAg/OGhXrvrnTN7+bC0H7l7l7Y377JPdjmWRqqIKJSXpB5MrN26lZtE6Plj0FTWLv2Luso00K5QI7NWrA9+p7kf1gM5U79KFXjd66tMMIFVxsTGqen7Ue6aKyE1pfxPGGGNMEhZgZWjCyN7c9Pw87nnzc+8B1rTCnJlqalbOu/8DXpu/msrWpbQtL6NdeRlty0tp2zrydZl7/OvX25WXsa1Z+XDxOmoWr+OLr5z6YW1alTKqfycuOnR3qgd0YVT/TrSvaJWt7i8TkV8A/3CfnwFYIpwxxpissAArQxWtSjlr/wH84cUFfLJyE3v0aJ/6TRMnwuTJ2e9cwP7vnUW8Nn81p+3bj3blZdQ2NLK5vona+kY21zeyYuNW92vn2JZtO07zVbVrTfUuXThr/13Yd0AXhvTuQKvSnKUBng5ch1OqQYE33GPGGGNM4GwVYQC+qm3ggN+/zPEj+nDjycNTv0EECuznvmRdHYf/8Q32HdCF+87Z11O+WVOzUtvQSG19I80KvTtW+M5TS7WK0BhjjMlHtoowAF3atuakffry1IdLWb2pPuzuBE5V+cW/ZgNww7eHeQ6SSkuEDhWt6NWxDX06tbFFAMYYY1oMC7ACct5BA9nW3Mz/vbMo7K4E7umPlvHa/NX89IhB9O1cGXZ3jDHGmLxnAVZAdu3WjvGDe/B/7y5mS6oSA0uX5qZTAfiqtoHrn5nLyH6dOGv/AWF3xxhjjCkIluQeoPPHDuSlj1fy5IdLOOMbuyQ+cdo0p5p7Afj1s3PZtHUbN540nNIMSjOERURux93gOR5VvSSH3THGGNNCWIAVoP0GdmF4345MeXMhp+/bP3GtqAkTCiLJ/bX5q3jqw6VcMn4PBvX0sDoyP4W/CsIYY0yLYwFWgESE8w4ayKWPzOCVeas4bEiPsLuUttr6Rq59aja7dWvLjw/dLezupE1V7w+7D8YYY1oey8EK2NF796J3xwruefPzsLuSkVtemM+yDVu48aThlJeVht2djIlINxG5RUT+KyKvRB5h98sYY0xxsgArYK1KSzjnwIG8t/ArZi3ZEP+ku+/Obad8+vCLddz39iK+N2YXqgd0Cbs7QXkQ+BgYCFwPLAI+CLNDxhhjipcFWFlw6n5OpfOEo1gTJ+a2Qz40NDbzs3/OomeHCn56xKCwuxOkrqo6Bdimqq+r6rnAuLA7ZYwxpjhZgJUFHSpacdq+/fjPrOW89/nanU/I44Kbd73+GfNXbuI3JwzL5r6AYdjm/rtcRI4RkVFA0QzPGWOMyS8WYGXJjw7dnQFdKznnvg94f+FXYXfHk09XbeIvr3zKcSN6M36vwk3QT+A3ItIR+AlwJXAvcHm4XTLGGFOsLMDKki5tW/PwxDH06ljB9//+ft4HWc3Nys/+OYvK8lKuO25I2N0JnKo+q6obVHW2qh6qqqNV9emw+2WMMaY4WYCVRd3bV/Dw+XGCrGOPDbdjcTz43mJqFq/jF8cMoapdedjd8UVEFonILBGZISI1Ma9d5f57u4jcFvsIp8fGGGOKnQVYWda9gxNk9XSDrA8WfQXPPBN2t3awbP0WbnxuPmP3qOKkffqE3Z10HaqqI1W1Oub4x+6/NcC0OA9jjDEmcBZg5UD3DhU84gZZZ//tfdaPPzLsLm2nqvzyX7NpalZ+++29kTxOwE+Hqkai2TpVvT/6AdSF2TdjjDHFywKsHIkOsjq98jw1i/IjJ+vZmct5ed4qfnL4nvTrUhl2d9KlwAsiMk1EEtXAuMbjMWOMMSZjogWwJ1409wN0IkD//v1HL168OOQe+bNq41a6d2zD0F9O5f5z9wu1kOe62gYO+8Pr9O3chid/dGBebuYsIouBNVGHJqvq5Jhz+qjqUhHpDrwIXKyqb7ivHQUcDXwHeDTqbR2AIaq6X1a/AWOMMS1SwY1gqepkVa1W1epu3bqF3R3funeoAKBHB2e6MKyRrPrGJn797Fw2bNnG704cnpfBlWtN5PftPibHnqCqS91/VwFPAdFB0zKc/Kut7Jh79TRwRLY7b4wxpmWyzZ7DoMrDG7dy+uR3Oftv7/PAefsxepfgR7JUlVWb6vls9WY+X13rPNY4Xy9ZV0ezwo8P3Y0hvTsEfu1cEZG2QImqbnK/Phz4VeR1Vf1IRGYDR9jGz8YYY3LFAqwwTJ5Mj4kTeXjiGE6b/C5nTcksyNq6rYnPV9d+HUi5QdTCNbVsrm/cfl5FqxIGVrVj774dOWFkbwb17MCRw3oG9V2FpQfwlJucXwY8pKrPRZ+gqk0i0k9EWqtqQxidNMYY07IUXA5WtOrqaq2pqUl9Yr4RAffnvnLjVk6b/C6rN9Vz/7n7MXqXzknfumHLNuYu28icZRuYu2wjc5dv5JNVm2lq/vr32KdTG3bt1pZdq9qya7d2ztfd2tGrQwUl+TsVGJeITItTeiGddh4A9sKZGqyNHFfVP2TatjHGGBPLRrBC1sOtk3X6Pc50YSTIUlVWbqxnzrINzIkEVMs38uVXW7a/t3v7cob07sD4vbozuGcHduvWjoFVbWnTujTE7yhvfeY+SoD2IffFGGNMkbMRrDBEjWBFrNiwldPvcUayRvXvxNxlG1lb+/Vs1sCqtgzp3YEhvTowtHcHhvbuSLf2hVVxPR1BjWAZY4wxuWQjWGF4euct8Hp2dEayLn54Ol/VNjB+r+4M7d2Rob07MLhXB9qV268qEyLSDbgKGApURI6r6rjQOmWMMaZo2ad2GEaPjnu4Z8cKHr/wgBx3psV4EKcO1rHAhcDZwOpQe2SMMaZoFVwdrKLQp2D3+ytkXVV1CrBNVV9X1XMBG70yxhiTFTaCZVqKbe6/y0XkGJwCpOGV0TfGGFPULMAyLcVvRKQj8BPgdpytci4Pt0vGGGOKlQVYYTj//LB70GKISAVOztXuQB9giqoeGm6vgiUiNwPHAQ04pSjOUdX1oXbKGGNaOMvBCsPknbbTM9lzP1ANzAKOAm4NtztZ8SIwTFWHAwuAa0LujzHGtHgWYIUhwSpCkxVDVPVMVb0bOBkYG3aH0iUiA0TkYxG5R0TmiMgLItJGVV9Q1cieSO8CfcPspzHGGAuwwjF9etg9aEkiye1EBSGFbA/gr6o6FFgPnBTz+rnA1Fx3yhhjzI4sB8sUuxEistH9WoA27nMBVFU7hNe1tCxU1Rnu19OAAZEXRORaoBGn5pcxxpgQWYAVhl69wu5Bi6GqxbYxY33U101AGwAR+T5OEdXxWsj7XxljTJGwACsMy5aF3QNTRETkSJxtgA5W1bqw+2OMMcZysMIxaVLYPTDF5S9Ae+BFEZkhIneF3SFjjGnppJBnE6qrq7WmpibsbvgnAgX8c88lEZmmqtUpzikFaoClqnpsbnpmjDHGJGYjWKYYXAp8HHYnjDHGmAgLsExBE5G+wDHAvWH3xRhjjImwACsMhTitmb/+hJPg3RxyP4wxxpjtCi7AEpGJIlIjIjWrV68Ouzsm+6oiv2/3MTHygogcC6xS1WnRbxCRChF5X0Q+ciueX+8eHygi74nIpyLyqIi09toJEekkIk+IyDy3mvr+ItJFRF4UkU/cfzv7aO9SEZnt9u8y95jn9kTkbyKySkRmRx272e3fTBF5SkQ6Rb12jft9zxeRI3y0OUlElrrJ8zNE5GivbSZob6SIvOu2VSMi+7nHRURuc9ubKSL7eP1ZGmNMXlLVgn2MHj1aC5JT4NJ4ANRogt8/8DtgCbAIWAHUAf/AKSLazj2nFfAeMAZ4DDjNPX4X8MNEbce51v3AD9yvWwOdgJuAn7nHfgbc6LGtYcBsoBKnVMpLOJtRe24P+CawDzA76tjhQJn79Y2R9wNDgI+AcmAgzobQpR7bnARcGefclG0maO8F4Cj366OB16K+nur+7sYA73n93djDHvawRz4+Cm4Ey5gIVb1GVfuq6gDgNOAVdfYdVFXd7J7Wyn0oMA54wj1+P3CCl+uISEecYGGKe90GVV0PHO+246s9YC+cAKJOne17XgdO9NOeqr4BfBVzLNGehMcDj6hqvaouBD4F9vPSZhIp20zQngKR6vkdgUhRuOOBB9zf3btAJxGxirzGmIJlAZYpSiJSKiIzgFXAizgjLOujApAlQB+PzQ0EVgN/F5EPReReEWkL9FDV5e45K4AeHtubDYwVka4iUokzetMvg/biid6TsA/wZdRrfr53gIvcabu/RU1bptvmZcDNIvIlcAtwTUB9NMaYvFLQldynTZu2RkQWh92PtIiE3YNCsYuXk1T1NeC1qOdNwEg3D+kpYHAGfSjDmeq6WFXfE5E/40zhRV9fRcRTcTNV/VhEbsSZLqsFZuBse5NWe7EC3pPwTuDXOCNPvwZuxQne0vVD4HJV/aeIfAdnVPCwjHtpjDF5pqADLFXtFnYfTH5T1fUi8iqwP860U5k7itUXWOqxmSXAElV9z33+BE6AtVJEeqnqcnc6a5WPfk3BnXIUkd+610i7vYgEexIuxRkhi/D8vavqyqi27wGezbDNs3HqlgE8ztflNdLuozHG5CObIjRFR0S6RVbQiUgb4Fs4hUhfBU52Tzsb+LeX9lR1BfCliAxyD40H5gJPu+34as/tV3f33/44+VcPZdKe21ZkT8IJuuOehE8Dp4lIuYgMBPYA3vfYZnQe1LdxpjcB9gV+5a6CTNVmVxGZKyIzcf6o+457fBxQ564yPAK4yl1NOAan7MZ/3JWW94tIQf8xaIxpeQp6qxxj4hGR4ThJ4qU4f0Q8pqq/EpFdgUeALsCHwJmqWu+xzZE4oy2tgc+BcyJtA/2BxcB3VNVTkriIvAl0BbYBV6jqyyLS1Wt7IvIwcAhQBawErsPJZyoH1rqnvauqF7rnX4sztdcIXKaqUz22eQgwEmeKcBFwgTvC9k2cQOl89/hObUa1181t7//hJPifi5NvVQGsAw5y+/05zpRprfueb6rqAhH5FbDYHfUzxpiCYAGWMSYtIjIAeFZVh7nPdwP+ihMc1QHnq+q8mPeMAv6iqgeKyE+BClX9tfvaFOB5nJHGd1V1N/f4WOAaVT0aY4wpEDZFaIwJymSchQCjgSuBO+Kccx5fr278CDhSRCpFpAo4FCcPaw1QJiKRTb5PZsf8LGOMyXuW12CMyZiItAMOAB6Xr1fIlseccyZQDRwMTt0uEdkXeBunDMY7QJO7gvI04I8iUo6z2nKHVZbGGJPvLMAyxgShBKfO2Mh4L4rIYcC1wMHReW+qegNwg3vOQ8AC9/g7wFj3+OHAntnsvDHGBM2mCI0xGVPVjcBCETkFtu8tOML9ehRwN87qxu2lJ9xisF3dr4cDw3FGq6JXWZYDV+NsbWSMMQXDktyNMb4lWHH4Ck5h0l442xM94q7efAnYG4hUqf9CVSeISAUw3T22EbhQVWe47d+MU8+rBLhTVf+Ug2/LGGMCYwGWaTFEZKKqTrb2irM9Y4zJJzZFaFqSidZeUbdnjDF5wwIsY4wxxpiAFfQUYavytlpe2SXua82tkr831esA2srbz6a0VbOn8yLalG3zdX609qVb03pfc7NSUuJ/g+nmZuXLj2u3P++3V9u02knX4jmb1/jZc7KqqkoHDBgQ97XVq1fTrVtw21cWRXvLlkHv3sG150M67U2bNs3X/WCMMWEp6DIN5ZVdGDnu0rivbe5dmvS9dT1Tt1/f01sg1LnnJk/nAexdtTz1SUkc3Hm+7/fcfdk8aqauofqoKi740+CU52+tbaKi7dc/P7/vD9L5g95a7Of8AQMGUFNTk63uFB8RJ8gqECLi634wxpiw2BRhhvI9uNpa20TN1DUA1Exdw9ba5PUa775sHhfv8w53X/b1DicX/Gkwt0/fP7DgKlUfjDHGmEJnAVaBSCe4AqhoW0r1UVUAVB9VtcPIVKxkwViy9/kRL4AzIerVK+weGGNMUbIAC2iur9/pmNfpQa8yHb3yKt7okNcRKD/BWLp98zOaZnKggKYHjTGmkLT4AGv5Iw/w2a+vYfkjD/h+r9fpwVxNDSYbHfIaLAU9HRjbBy8BXCTwen3doMD7YGJMmhR2D4wxpigVZYCVKsE9orm+ns2zZzjvmT0j7khWptIJrrbV+R898zs6lOz1oEeuoqUK4CJB4m9+tDRrfTBRrr8+7B4YY0xRKsoAK5XICsKS8nLaDRsJQLthIykpLw+vU643r32FR8c9wJvXvgJ4H73yM70Xdh5UspGrSJC4+OWFaQWaxhhjTD4o6DINQeh12lk015/qO7iKnR5s2tJAaZvWOxzzO3q1rW4bi19eCDgBxpU3zwW8jyZd8KfBnH1D6pGr6JGus29oCnTEKrbEgx+RILFm6hp2GT+QVpUeipUZY4wxeShrI1gi8jcRWSUis6OOjRSRd0VkhojUiMh+7nERkdtE5FMRmSki+2SrX/HEBld+E9wX3fgks0+5iUU3PplRP1pVtmKX8QMB2GX8wLQClfuv/STp6FQ2E9mDGBkbfN3xnPrKWYy9YVxg/TJJWM0wY4zJimxOEd4HHBlz7CbgelUdCfw/9znAUcAe7mMicGcW+xWopi0NbHhzLgAb3pxL05aGjNobe8M4Tn3lLH5xRx/f7/Wah5VJIvvr6wZtf6Rz7VRtAzZyZYwxpuBlLcBS1TeAr2IPAx3crzsCkTXixwMPqONdoJOI5G2BnujpwdI2rek4dggAHccO2T5NmMnKwXQDjPuv/WT716lGp9IZuYoNqqKfV7QtpXNP53vv3LO17/ZtxWBIqqvD7oExxhSlXOdgXQY8LyK34AR3B7jH+wBfRp23xD2Wm+JRGRpw9Yk0XXLsTjlY6Tq483zfuUzRI0gATY3B7jGZKAB6fd2g7f1dt8IZvVu3osFX/y24MsYYU2xyvYrwh8DlqtoPuByY4rcBEZno5m/VNNZvDryDXsSbBgwyuEonl6mibSmjvtV1+/MPX1yb00Ke2S5SakzQRKSHiEwRkanu8yEicl7Y/TLGFIdcB1hnA5FM8MeB/dyvlwL9os7r6x7biapOVtVqVa0uK2+30+tBbPKczJq7HkyZ0J7J9GAmuUw/+ste24OsXAU50WUkslmk1GTJddeF3YMw3Qc8D/R2ny/AGWU3xpiM5TrAWgYc7H49DogkDT0NnOWuJhwDbFDVvJsebN5aT13NTCCYhPZYB3een/ZI0GGVnwJOkJWNIMdPPS5TQFp2JfcqVX0MaAZQ1UbA9m8yxgQiazlYIvIwcAhQJSJLgOuA84E/i0gZsBVnxSDAf4GjgU+BOuCcbPUrEyUV5VRWD6euZuYOCe1BiB0J8lOfKhJcHVb5KS/V7e7pfZnUqzJFpHfvotuPUEQuB36As6hmFnCOqm6Nc2qtiHR1zyPyx13OOmqMKWpZC7BU9fQEL42Oc64CP870ml63yMlE1YVn0KHjkYEGV/FkM/i5+7J51ExdQ/VRVRmNdHkd1TJ5bHneDRRnRET6AJcAQ1R1i4g8BpyGMx0Y6wqc0fPdROR/QDfg5Fz11RhT3FrkVjmZCjq4yiRQiYxe1dY27/A8kSDqVQXNtsQxASsD2rgj5ZV8XQ5mB6o6HSdl4QDgAmCoqs7MWS+NMUXNAqwCFgmmLvrheoYNXsVFP1y/w/GI6CAqk9V+0YFgUKNXd182b4e9F9MRvbJ0+ao1qd9gvrZPTjdNCEJV5HftPiZGv6iqS4FbgC9wyrxsUNUX4jUkIj8G2qnqHFWdDbQTkR9l+xswxrQMFmClYd2K9hm3ERm1yTRQqa1t5j/POukl/3l2604jWfFKPuTLar+gNneOXlm6obmcNZvrg+xmcZs2Lewe+LUm8rt2H5OjXxSRzjiFiwfirA5sKyJnJmjrfFVdH3miqutw8kSNMSZjRRNgBZl/Vb4iu1u1vHntK0lHbbxM20UCqLZtSzjm2AoAjjm2grZtv/6V7q8L8m46MFr0aFpQmzs3NjXzowens62pOeO2WoSJE1OfU1gOAxaq6mpV3YZTFuaABOeWiohEnohIKZDd5EpjTItRNAGWF5nWwArCtrptLH55IeCM2sQGPV6KjMZOAf7lzk7Mntedv9zZaYfjbduWxJ0OzGRT5oM7zw80uf2CPw0OdHPnvp0reX/hV/zqmbmBtFf07rkn7B4E7QtgjIhUusHTeODjBOc+BzwqIuNFZDzwsHvMGGMy1qICrHzQqrIVu4wfCOycA5VJAnr0yFW0xyeX7TAdmOgaYY5uBbm5c6fKVkz85q7837uLefj9LwJr1xQGVX0PeAKYjlOioQSYnOD0q4FXcXaY+CHwMnBVDrppjGkBLMAK2N5Vy7fnEiXKKRp7w7i4OVBeEtBTrRKM59huC5NeI5MRrXx09ZGDGbtHFf/v37OpWRS737gpdqp6naoOVtVhqvo9VY2blKeqzap6p6qe7D7uVtX8mkc3xhQscUpQFaZ2nfvpyHGXes6/8jNFWN8zecJ1556b4h5f/8cHWfzyQiq7t6VuVS27jB+40/RXqim2ZEVAo8syJBq1SuSlut13usbW2iYu3ued7cdvn75/zguQJtvs+R9jpkxT1WqvbVVXV2tNTQ0b6rZx/F/fYnN9E89cfCC9OrYJpK9FZ9kyp9hogRARX/dDirYOBCYBu+CUdhCcsny7BtG+MaZlsxGsBNJJdG/a0rA9v6puVS2Q3uq4VAFObFkGL6KDq+hrhL1Jc7LgKhMdK1sx+axqtjQ0csH/TWPrNhuYiKvwVhEGaQrwB+AgYF+g2v3XGGMyVhQBVrtl3j48K1f4azdZkBWvVENpm9Z0HDvEuVb3tkD81XGZBBXPrh4YtyxDPC/V7b79kYzXsg2vrxu00yMT2QquIvbs0Z4/njqSmUs28PMnZ1HIo7VZM2FC2D0I0wZVnaqqq1R1beQRdqeMMcUha1vl5Fq7ZU2epgorV2R3NeGAq09k8PXVtKpsxba6bQkTuF9fNyit1XiREafIVjfvyJ4cxtd5WamCqWTtJpIsEIp+zev3k+3AKtrhQ3ty+WF78seXFjCkdwd+MNZmf8x2r4rIzTilHLbnabkV3o0xJiPZ3Oz5b8CxwCpVHeYem4RTyG+1e9rPVfW/7mvXAOfh7GZ/iao+n62++Qmyyle0SpiPtW5F+7i5WPPq+rN35fKUq+PSDbJiN4NON6hKxW8g5CXYymVwFXHxuN2Zu3wDv/3vxwzu2YGD9qjKeR9MXvqG+290TpcCwdQMMca0aNkcwboP+AvwQMzxP6rqLdEHRGQIzoasQ3GqL78kInv6XdHjdRQLgguyMpXJSFY2hTX9l2zUL10lJcKt3xnJiXf8jx8/NJ2nLzqQXbq2DfQaBevuu8PuQWhU9dCw+2CMKV5Zy8FS1TcAr2vkjwceUdV6VV0IfArsl851veZjgb+crGxXd88XQeRWpStVhftMtCsv456znIGKiQ9Mo7a+MfBrFKTiq+TumYj0EJEpIjLVfT5ERM4Lu1/GmOIQRpL7RSIyU0T+5u4bBtAH+DLqnCXusbRkK8iKJ9G+hLPW9PLcRlgBTWwfwuxHbIX7dPclTGaXrm35y3dH8cmqTfzksY9obrakd77eKaYlug94HmfUHGABcFlYnTHGFJdcB1h3ArsBI3F2ur/VbwMiMlFEakSkprF+c8DdSy6bo1hhBTdhB1YR0RXug9qXMJ6xe3Tj50fvxXNzVvCXV/0XbTVFpUpVHwOaAVS1EScH1BhjMpbTVYSqujLytYjcAzzrPl0K9Is6ta97LF4bk3G3vmjXuV/CIYhs5WPFkyjZ3a9087HSvZZX2ciLimfsDeMYc232r3XeQQOZs2wjf3hxAYN7tufwoXmwSaUJQ62IdMVJbEdExgAbwu2SMaZY5HQES0Si582+Dcx2v34aOE1EykVkILAH8H6m18vGVKGfUSw/04QR2RhNyqR+VTbzouLJRSAnIvzuxL0Z3rcjlz86g09WZh4cF6xjjw27B2G6Auf/PbuJyP9wFuRcHG6XjDHFIptlGh4GDgGqRGQJcB1wiIiMxPmLcRFwAYCqzhGRx4C5QCPw4zD2BMtkJCvRKNasNb3Yu2q5r7YSBUC5rjMVmxeVi9GlXKloVcrd3xvNcbf/j4n/N42pl46lolVuq9jnhWeeCbsHoVHV6SJyMDAIZ5uc+aqaneXCxpgWJ2sBlqqeHufwlCTn3wDcEHQ//EwVQvYLkWYi17lSkbyoxS8vzGpeVFh6dWzDn08byRn3vsftr3zCT49IXsm+KB13XIsLskTkxAQv7SkiqOqTOe2QMaYoFU0l92SCDrL81sVKZxQrX+QqLyqedKZY/Tpw9ypO3KcPd7/+OceP7MOePeKvCi1azz6b+pzic5z7b3fgACAy/30o8DZOZXdjjMlIUexFmA3plG9IVLIBdgwW2q3fmk6XQpPr4GrWml45Ca4irj16L9pVlHHtU7OsdEMLoKrnqOo5QCtgiKqepKon4RQ6Lq5hWmNMaCzACsH3/vBO2F3IOa91rXIZWEV0bVfOz4/aiw8WreOxmi9Tv8HkLREZJCIzoh4bReSyBKf3U9XooeWVQP/s99IY0xJYgJVAOnlYyUo1RKYIe36xgQNe+JweX7Sc1eBeVyKGEVxFnFLdl/0GdOF3U+exZnN96jcUCy2uETtVna+qI1V1JDAaqAOeSnD6yyLyvIh8X0S+D/wHeCk3PTXGFDsLsLJMmpU2Wxoor9tGed02vvHS5wCMeenz7cfK67YhRTo15aVCe66nBOMREW749jDqGhr57X8+DrUvOTV5ctg9yKbxwGequjjei6p6EXA3MMJ9TFZVK9NgjAlEi0hyD5qfBHdR5ewn3+H8x96itOnrIOrkydM5efJ0mkqFf58zkn+dMxJnpXhxSbUSMYjASkQmAhMB+vdPf4Znjx7tueCbu/GXVz/lpNF9OXD3qoz7lvcuuKDQ9iOsEpGaqOeT3eLD8ZwGPJysMXfFoCW1G2MCJ1rAUwTtOvfTkeMu9Xy+n5WEyaYIEwVYiaYI965azqAPl3Phda9Rtapu+/E13Su581eHsmBk/Iv5qeqeD9vdRIut/h6vGvysNb1o2tJAaZvWCdv56NjffKiq+3i9bnV1tdbU1KQ+MYGt25o44k9vUCLSMmpjiRTUNKGITFPVag/ntQaWAUOjd5CIOedE4Eac1YTiPlRVOwTYZWNMC2VThHEEnX8FMH9UL178ztAdjr146rC4wdXBnef73jIn1XuysXlyIvFyrqKDq8iU4KIbn2T2KTex6Mb4Awju8VFZ7u4OKlqV8psThrFwTS13vPZZLi9tgnUUMD1RcOW6CZigqh1VtYOqtrfgyhgTFAuwfPIzPRir+vXF1JeX8uJxe9JQXsro1xbtdE6mexHGC7Ryud1NqpyryJRg05YGNrw5F4ANb86laUvDDudFv55rY/foxvEje3PXa5/x6arcbiiec08/HXYPsuV0UkwPAitVtQUl3BljcskCrCyLrB7svKqW8rptTPrbBB64dizX/W0CFXXb6LS6NivXjQRaXpLMgxTJuQJ2yrmKzrcqbdOajmOHbH/+5W07FryMfT3XfnHMECpalXDtU7Mo5Gn0lEaPDrsHgRORtsC3SJ1bVSMij4rI6SJyYuSRgy4aY1oAC7BypKSpmUlTJrBkty4ALNmtC5OmTNgh8T3T0at4DuvzOdVHOcnaudruZsy1Yzn1lbMYe8M4IPEqwX6XfL3RcLxRrAFXnwjwYTb7mki39uX87Ki9eG/hV/xz+tIwupAbffqE3YPAqWqtqnZV1VS1UDrglHE4HKe6+3FAi9792hgTnGxu9vw3nP9ZrVLVYe6xm3H+J9YAfAaco6rr3deuAc4DmoBLVPX5bPUtmWztQ7i2185V3rdVlLG2Z7vsXDDKBX8azNk3NFHRtpTX12X3Wm9e+8r2FYOdLj8j6bmRUaoNb86l49ghiZLdm7PSUQ9O27cf/5y+hBv+M5dxg7vTpW3iZHxTeNxq7sYYkxXZHMG6Dzgy5tiLwDBVHQ4sAK4BEJEhOEuqh7rvuUNE0l6+1dSYnUKRflcP+nFw5/lsrW3a/jz66yBUtC3dfp1sjJTBzvlXsSNS8Qy4+kSGPX5VZLQqr5SUOLWxNm1t5Hf/tVSdYiMie4rIyyIy230+XER+EXa/jDHFIWsBlqq+AXwVc+wFVW10n74L9HW/Ph54RFXrVXUh8CmwXzrXnffeP3j36V8w771/pNnzcNx92Twu3ucd7r5s3g5fByFesJaNIKtVZaukeVWJJCvTELbBPTvwg7G78vi0Jbz7+dqwuxO8888Puwdhugfnj7xtAKo6E+cPPWOMyViYOVjnAlPdr/sA0ZvALXGP+dLUWM/apR8BsHbpR1kbyfIqkuCeyjdaz6Vm6hoAaqau2eHrTEeykgVrQQZZkTyrVHlVhejS8XvQt3Mbrn1qFvWNwY4shq64K7mnUqmq78cca4x7pjHG+BRKgCUi1+L8j+zBNN47UURqRKSmsX7HJfSlZeV07TMCgK59RlBaVr7D6+2WJf9wTKe4aBAq2pZuT0SvPqpqh68jU3vp2FrbFGiwFi0SUMUmsEev/kuSV1VQ2rQu5dcnDOOz1bVMfv3zsLsTrCJcRejDGhHZDVAAETkZ8PZXkTHGpJDzrXLcTVWPBcbr1+vflwL9ok7r6x7bibstxmRwKrnHvj74G2fS1HjKTsFVGOJVL48VGUWKTkQHdvg6XZHArWbqmoyDtQgvW9sMuPpEmi45tiiCq4hDB3XnmOG9uP3VTzl2RG8GVrUNu0vBmD497B6E6cc4/y8ZLCJLgYVA8pUZxhjjUU5HsETkSOAqnOrJdVEvPQ2cJiLlIjIQ2AOIHbr3LFlwlWwUq3JFulfc2aIbn/Rd3DM6AAoiGAIncLt9+v5c8KfBO70W1PY68aYBiym4irju2CGUl5bwy3/NLu7aWC2Eqn6uqocB3YDBqnpQoo2hjTHGr6QBloh0EJHficj/ich3Y167I8V7HwbeAQaJyBIROQ/4C9AeeFFEZojIXQCqOgd4DJgLPAf8WFULItkl3grC6CrkuSjumUpQwVq80atU292ka92K9qxbsXNpizB171DBVUcO4q1P1/D0R8vC7k4wemW+2XahEpGuInIb8Cbwmoj8WUS6ht0vY0xxSDWC9XecDVD/iTPC9E8RiQwPjUn2RlU9XVV7qWorVe2rqlNUdXdV7aeqI93HhVHn36Cqu6nqIFWdmqztfBedh5Sr4p5++R29ihdcpdruJh35GFhF++43dmFEv078+tm5rK8r/AR+lhVJoJieR4DVwEnAye7Xj4baI2NM0UgVYO2mqj9T1X+p6gRgOvBKof+VlyrZPQjH3bz/DtXM/Qi6Bla2BJnQnu+BVURpifDbbw9jfd02rv7nzMKfKpw0KewehKmXqv5aVRe6j98APcLulDGmOKQKsMpFZPs5qnoDTu2YN4DQg6zShvwORNIZuQq6BlY8QYxeRQRRKLQQAqtoQ3t35GdHDeb5OSuZ8tbCsLuTmeuvD7sHYXpBRE4TkRL38R0glB0kjDHFJ1WA9QywwxCMqt4H/ARnu5vQtf0iO5sl50JsHapsllXwKjZfzMuqwXQVyqhVPOcdNJDDh/Tg91PnMW1xlvcfMtlyPvAQzv/L6nGmDC8QkU0isjHUnhljCl7SAEtVr1LVl9zVfd8VkZ+LyP/DqbL+f7npYssRXQ9r1Le6JkxOzyTwSjZ69ea1r/he+ZhOknshB1YRIsLNp4ygV6cKLnpoOl/V5sXfG8YHVW2vqiWqWubmipa4x9qraoew+2eMKWxeyzT8G2c7m0ag1n1sTvoOk5YL/jSYUd/qyocvro07TZitKcTYfQS31W1LOXrlN8m9GAKraB3btOKO745m7eYGrnhsBs3NBZiPVVMTdg9CI44zReSX7vN+IpLWFl3GGBPLa4DVV1VPVdWbVPVW9/GHrPbMh3SmCXOR6J6OrbVNfPiis+dd7DRhplOIyUavWlW2YpfxAwFn5eO8uv4p2/OS5B4JqoopsIq2d9+O/L/jhvDa/NXc+fpnYXfH+HMHsD8QKUGzGfhreN0xxhQTr5Xc3xaRvVV1VlZ7UyS87kEYT7Lq69GvJZtCjCc6uEpUYX7sDeMYc63z2owvGzytCoxUbd+4oSvrAizUWkjO+EZ/3l/4Fbe+MJ99+ndm/91CX//hXXU1FPpKyBgi0gm4FxiGsw3Ouar6TpxTv6Gq+4jIhwCquk5Eiq9CrjEmFF4DrIOA74vIQpxkUAFUVYdnrWc+tf2iltr+ud2+JF6R0SDEbpsT+1pT48fbpxDjVWhP5s1rX2HxywvZZfzAuCUkWlW24pmfvsOGN+fSceyQuKsDi3U0Kl0iwm9P3JvZyzZw8cMf8t9LD6J7+4qwu9WS/Rl4TlVPdgOmygTnbRORUr7ei7Ab0JyjPhpjipzXKcKjcLavORw4DmcvweOy1alCE0SBzVjRwVXsNGGiKcREIqNX8fKsYs34smvSvCoLruJrV17GnWeMZnP9Ni55+EOaCjEfqwiISEfgm8AUAFVtUNX1CU6/DXgK6C4iNwBvAb/NRT+NMcXPU4ClqovjPbLdOb+CKNngZz/CdSvaZ22rmIjYpPbolYapNnB+fd2gHaYGY/Os4k0TJsurytfgSkQmikiNiNSsXr06tH4M6tme35ywN+9+/hV/emlBaP3w5brrwu6BX1WR37X7mBjz+kCciux/F5EPReReEYk7tK2qD+Lsjfo7YDlwgqo+ntXeG2NajJxu9lxsmrfWxx3t8Vo7KlXBz0RJ7ck2cI60m8lGzkEUD80lVZ2sqtWqWt2tW7dQ+3Ly6L58p7ovt7/yKa/NXxVqXzwpvEruayK/a/cxOeb1MmAf4E5VHYWz4vln0SeISJfIA1gFPIxTD2ule8wYYzIWSoAlIpeKyGwRmSMil7nHuojIiyLyiftv52z3I9OVhCUV5YFtFeN3RWCikatkgVWqKcLowDCT7yUo5Svybw9HL66fMIzBPdtz+aMzWLZ+S9jdSa5377B7ELQlwBJVfc99/gROwBVtGlDj/rsaWAB84n49LUf9NMYUuZwHWCIyDKeC8n7ACOBYEdkd56/Ml1V1D+BlYv7q9Cpbld0TfdhnOtrz+rpBCWtbVbQtpXNPJ9Dp3LO1r+nAeLxMESaSy+nB8hWtCja4AmjTupQ7ztiHhsZmLnpoOtua8jhvenn6K17zkaquAL4Ukch/DOOBuTHnDFTVXYGXgONUtUpVu+Lklr6Q0w4bY4pWGCNYewHvqWqdqjYCrwMn4hQyvd89537ghBD65tu6Fe0zGu3ZVrctYW2rrbVNrFvR4F6nIe4oVySwipewHs/YG8alvQl1LhRyYBVt127t+P1Jw5n+xXpunJq9fSVNXBcDD4rITGAkiRPXx6jqfyNPVHUqcED2u2eMaQnCCLBmA2NFpKuIVAJHA/2AHqoa+XN6BSHuau8n0T0Rr3lY0aNKiepexXsNvp4O9LvFTbyRq2T9zcXoVaGPWsVz3IjenLX/Ltz71kKem52nRcL2iZ09K3yqOsPNzxquqieoaqLNIpeJyC9EZID7uBZYlsu+GmOKl9c6WIFR1Y9F5EacofhaYAbQFHOOikjcde7uqqGJABWtO8a9Ri5qYjVvraekojyQtsbeMI4rb56bsO5VbE2s2KKh0XlVkUKhhaTYAqto1x6zFzO+XM9Pn/iIIb060L9ropJMIZnWolOOTgeuwynVoMAb7jFjjMlYKEnuqjpFVUer6jeBdThJpitFpBeA+2/cJVjRK8Zal+X2wyoSCKy560GWXPT/WHPXg4G1/V7DkISvJQquILO8Ki+yOXpVjKNWscrLSvnrd/dBgB89NI2t2/Jsi6aJsVUOWg5V/UpVL1XVUaq6j6pepqpfhd0vY0xxCGsVYXf33/44+VcPAU8DZ7unnI2zwXTead5aT13NTADqambSvLU+J1NoyZLYM82r8jqdGaRiD6yi9etSya3fGcnspRv56RMz2dKQR0HWPfeE3QNjjClKYdXB+qeIzAWeAX7sVlr+PfAtEfkEOMx9njavqwn9lmooqSinstrZIaiyenhg04SQuMSCl5pW2ZgWzEbg2BJGreL51pAe/PSIQTzz0TKO+8tbfLx8Y9hdMsYYk0U5z8ECUNWxcY6txVlSnRcqV0Bdz/ivVV14Bs1bT04ZXM1a04u9q5Yn3Fw5lUyKhaZ7zWxpiUFVrB8fujvD+3bkisc+4vi//o+fHzWYsw8YgIiE3TVjjDEBCyXAKmTlK1pR33Ob55GrVJsrx3p93SAO7jx/e+mFdIIkP9dMND3odfTKAid/xu7RjamXjuWnj3/EpGfm8tana7jp5BF0aRtSYdelS8O5bohE5HbcDZ7jUdVLctgdY0yRKuqtcrJVdNSrpi0NCSunJ6tb9fq6Qb5LL0S3m2pD51TXTyYyxddSp/qCUNWunL99f1/+37FDeGPBGo780xu8/emacDrTMlcRRqq4J3oYY0zGijrAypTfjZ9jRW+cHL3CL1XwFC9I8hoQpVpVOGtNL5756Ts8Ou4BnvnpO56/F7DRqiCJCOceNJCnfnwA7SrKOGPKe9z03LzcV32fMCG318sDqnp/skfY/TPGFAebIsRJdN/cO/E2NLEi04ReDLj6RJouOdat9r7cU92qSJAUmeZ794Y3k075xU7zdbr8DNpf2EBpm9bMihkYadrSsOMG1dv75rDgKreG9u7IsxcfxPVPz+WO1z7j7c/Wcttpo/KvXlYREpFuwNXAEKAiclxV83ObA2NMQbERrBTSGcVq2tKww/FIADNrTS/PdasipRfGXDvW8wbN8a4Z73hQG1SbYFS2LuPGk4fzl++O4rPVmzn6tjf594yWlxsVggeBj4GBwPXAIuCDMDtkjCkeNoKVpkSjWItufJINb86l49ghcTeAnrWmF2NvGOep4nrk9ejRrMixTGpX7Tiq9jUbvQrXscN7M7JfJy59ZAaXPjKDNz9Zw/UThtK2PIv/md59d/bazn9dVXWKiFyqqq8Dr4uIBVjGmEBYgOVKNk2YrGRDtOat9Umn3yIi5Ru8ig3IgigMmuvgKoj9HVuCvp0reXTiGG575VP+8sonTFu8jttOG8XefeNvC5WxqEruG7duo66+iZ4dK5K8oahE/kJaLiLH4OxD2CXE/hhjiogFWBmIHcWKFCGtq5mZcvrNb5DlN7hq2tLgefrPb3BlwVJ2lZWWcMW39uTA3bpy2aMzOP6vbzF2j26cNLovhw/pQUUr7/mCKYnw9iereXzaEqbOXo4gPHT+NxjVv3Nw18hfvxGRjsBPgNuBDsDl4XbJGFMsRDVhOZi817Ftbx0z5IKk5/jd9DlZsnu8Uax404TNW+vpOqBh55Pj8Bpk+Rm1SjVNGS3fg6uP/nLFNFWt9np+dXW11tTUZLNLObW+roF731zIk9OXsGzDVtqXl3HM8F6cNLov1bt0TrtI6ZJ1dfxz2lIu/daeDLj6WdpXlDFhRG/e/GQNm+sbeeLC/dm1W7uAv5vMiYiv+8EYY8JiI1gZipeL5RQhTRxgRY8uRQKnZIGWn+Aq1SrBTNjIVe51qmzNlUcM4opv7cm7n6/lielLePqjZTzywZfs0rWSE0f15cR9+tCvS+pVh1u3NfH8nBU8XrOE/322BlW4FPjzaSM5YmhPKlqVsnBNLSfd+TZn//19/vnDA+jevvimC0XkKlW9KVHBUSs0aowJggVYPnjNxUom0ehSvCnDdHKtStu03j5NWVk9nI0busIGf23EG72y4CpcJSXCAbtXccDuVfz6+Eaem72Cf05fwp9eXsAfX1rAfgO7cPI+fTlq7560r/j696eqzFq6gcdqvuTfM5axaWsjfTu34dLxe3DSPn1hzrEcP7LP9vMHVrXlb9/fl9Mnv8u5933AIxP3p102k+yzQEQWAZuAJqAxzojXx+6/xTPUaYzJOzZFGIffaUKIP1XYueemHZ43bWlg9ik3bX8+7PGrdhpdigRZfoKreNN8zVvr09qIOt+Cq5Y+RZjK0vVb+NeHS/nntCV8vqaWilYlHDm0J8eP7MNnqzfzxLQlzFuxifKyEo4a1pPvVPdjzK5dKSlJPrX46vxV/OD+Gg7YrStTzt6X1mX5UdHFyxShG2BVq2rS8vgicoqqPp7qmDHGpCOUP01FpBNwLzAMZ4j+XGA+8CgwAKcezXdUdV0Y/Usmk1GsSA2qyAhWohWGqUSmGJPtF5gvwVW7ZU07PPdT0NWk1qdTG3586O786JDd+PDL9fxz2hKe+WgZ/5qxDIAR/Tpxw7eHcezw3nRsEyev7rjj4Jlndjp86KDu/O7EvbnqiZn87J8zufU7I4pxU+prgNhgKt4xY4zxLZQRLBG5H3hTVe8VkdZAJfBz4CtV/b2I/AzorKpXJ2snWyNYEEyyO+w8igWJV/h5WfkXmWKsrB5O1YVnJD3Xj3ST2mMDqHQk+1l7GcESkYnARID+/fuPXrx4ccZ9KmRbtzXxzmdr6dO5DXv2SLFptwgk+X/A7S9/wq0vLuDCg3fjZ0cNDrin/onIYiB6ZGqyqk6OOWchsA7nj7e747x+FHA08B2cP+oiOgBDVHW/bPTdGNOy5HwEy10W/U3g+wCq2gA0iMjxwCHuafcDr+FsY5F34o1ixUt2b95az7oV7XcKsuIFUV5W/kUnsNfVzKR568lpjVTF8hNcBRFQpWrT7yiX+wE6GZwpwsA6VqAqWpVy6ODugbR10bjdWbFxK3e9/hk9O5Tz/QMHBtJuBtZ4mDI+SFWXikh34EURmaeqb0S9vgwn/2oCO27uvAkr02CMCUgYU4QDgdXA30VkBM7/4C4FeqhqJMt7BdAj3pujRysqWqcuvtj2i9q0RrH87k8IOwZZa+56cHuiOe5IU7zRLPC+8m/jhq47JLAHEVwlkqvgKp5cXcekJiL86vhhrN5Uz/XPzqV7hwqO3jvzQrfZpKpL3X9XichTwH7AG1GvfyQis4EjbHNnY0y2hJG5WgbsA9ypqqOAWuBn0SeoM28ZdyRCVSerarWqVrcu87YhbtsvatPqaLIP+mRTZ81b66mrmQlERprqAScZPTpvKrJn4Ze3Pbv9WKLcrMj7qi48g75/+VVg04P5ltRucsxDikBpiXDb6aPYp39nLnt0Bu99vjYHHUuPiLQVkfaRr4HDgdmx56lqE9DPTVEwxpjAhTGCtQRYoqrvuc+fwAmwVopIL1VdLiK9gFVBXjQSZPkdzfK7hY4zikXSkaZ1K9pvH+HqcMBgNr49b/tr/S45dqdzY2Vz5CoRG1UqUpMn77BdTiIVrUqZcnY1J935Nj94oIYnLjyAQT1T5HelSVWpb2ymrqGJuoZGtjQ0uV97ugd7AE+5CfllwEOq+lyCcxcC/xORp3H+0Itc/w8ZfgvGGJP7AEtVV4jIlyIySFXnA+OBue7jbOD37r//zsb105ky9DtdWL6iFVUXnpEwRyp6hGvj2/Nos88wtkyfvdPoVbJVgl76kA4bvWphLrjAU4AFTtHT+8/dzylE+rf3efJHB9C7Uxtfl1u1cSvvfL6Wtz9dy5L1ddQ1NO0QQG1paKRuW5OXgbW4VPVzYITH0z9zHyVAdqJFY0yLFVYFwYuBB93h+c+Bc3D+J/eYiJwHLMZZ4ZMVQQZZyco2JBppit6zMLIa8Ou6VZt8BVZBbcacjI1emYi+nSu575z9+M5d7/D9v7/P4xccQMfKxPfghrptvPP5Wt75bA1vf7aWT1ZtBqBDRRm7d29Hu/IyurUrp7J1KW1al1HZutT9upTKVqVUti5zvnaPHXhjcN+Lql4fXGvGGLOjUAIsVZ0BxFsJND5Xfch2kNVqcTPbdkmc4pZohCvM4MpGr4wXe/XqwN1njebsv73P+f9XwwPn7rd9A+q6hkY+WLSOtz9bw9ufrmX2sg2oQptWpew7sAsnj+7LAbtVMaR3B0pTFDvNNhHpBlwFDAW27wmkquNC65QxpmgU1h4YAUs3LyuV5Y88wObZM2g3bCRdLjs94XmR4Cp6xaGX5PVsjFolCq7SHb1KtrAg6J+3ycDTT6f1tgN2q+LW74zkkoc/5KKHPmRo7w6889laPvxyHdualFalwqj+nbl0/B4csFsVI/t1yptq8FEexKmDdSxwIU5qwupQe2SMKRotOsCK8DOalWoUq7m+ns2zZwCwefYMeiw+lZLy8oSFSHdecZi4tlUupgOj+Qmu/KzUzFZga9IwenTab50wojerNm7lN//5mJfnrWTvPh0576BdOWC3rlQP6Exl67z/30tXVZ0iIpeq6uvA6yLyQdidMsYUh7z/P2CupFsvK5oTZJXTbtjI7SNYJeVOsBQdHEUHW7H5WGEEV+lMDaZb+iJeGxZohahPH0+lGhL5wdhdGbNrV/p1rkyai5WnIv8hLheRY3AKkHYJsT/GmCJiAVYUr0FWqlWFvU47i+b6U7cHV7EiwVIk0Eq24jDXo1YRiUavggisErVpgVZhGtYndcHfPPUbd2eJnwC342yVY5XcjTGBKOwAq34bsnApOrBPYE1mGmRFpgoTBVfRdhzVSvxaNuVTYrsFWiYXRKQCJ+dqd6APMEVVDw23V9klIgOAA1T1obD7YkxLUdgBlksWLgUILNDK9IM+WemGRMIYqfKb2J6N0aswr2OA888PuwdhuB9nevBN4ChgCM52XcVsAPBdwHOAJSJlqtqYtR4ZU+SKIsCKyEaglSjIamqsp92yct/7FWZDLkahLOgpUpMnh92DMAxR1b0BRGQK8H7I/QG2jzJNBd4CDgCWAser6pY45+4G/BXoBtQB56vqPBG5D9iIUwanJ3CVqj6BU8B5LxGZgRNg3uYeOwQoB/6qqneLyCHAr4F1wGBgz6x8s8a0AHm3bjoIkUArCPECi3nv/YN3n/4F8977R8LRnlwEPZUrgr+OFRVtYTJYRVjAtq8yycMRmj1wgp2hwHrgpATnTQYuVtXRwJXAHVGv9QIOwik/8Xv32M+AN1V1pKr+ETgP2KCq+wL7AueLyED33H2AS1XVgitjMlBUI1jRghzNih7JamqsZ+3SjwBYu/QjmhpPAeJvOp3OVKFX+ZQ7ZQrY9Olh9yAMI0Rko/u1AG3c54Kz13yH8LrGQrcQM8A0nKm9HYhIO5wRrsfdPRfBGYWK+JeqNgNzRaRHguscDgwXkZPd5x1xgrsG4H1VXZjJN2GMKeIAKyJ6NCuTYCsSZJWWldO1zwjWLv2Irn1GUFpWnnJD6Igggq1sBlZh514ZkwuqGv68fmL1UV83AfE2eywB1qvqSA9tJCqXLzgjYM/vcNCZIrT/4I0JQNEHWNEyHdWKBFmDv3EmTY2nUFr29R+NXjaEjgRH6QRaNmJlsqJXr7B7YHxS1Y0islBETlHVx8UZxhquqh8ledsmdtzQ+nnghyLyiqpuE5E9cXK+jDEByXkOlohUiMj7IvKRiMwRkevd4wNF5D0R+VREHnU3gs5OHzLI0YqM5kQHVxFe85f85k7lIriy0asWatmysHtg0nMGcJ6IfATMAY5Pcf5MoMn9/+7lwL3AXGC6iMwG7qaF/cFtTLaJZlDFOa0LOn9ttVXVzSLSCmfFzKXAFcCTqvqIiNwFfKSqdyZrq2NZN92/07cz6k+6o1nJSjiks7Iw3qhWLket8jXAeqFm0jRVjbcxeFzV1dVaU1OTzS4Vl0mTnEeBEBFf94MxxoQl5yNY6tjsPm3lPhQYBzzhHr8fOCEX/QlyxWFEOivxoke1srE6MJlsrxyUhUuz8nM2Abj++rB7kBUiUioiH4rIs2H3xRjTMoVSpsH9n98MYBXwIvAZTtJmZMn0EpwKy/HeO1FEakSkpkG3BtOfNAKAbI3s5FOuVSbfY+RnGv1ztSDL5NClwMdhdyJTIvJXEZkR8zgn7H4ZY1ILZc5dVZuAkSLSCXgKp6Cd1/dOxqkBQ8eyboHOb/pNgk9WiNRL0ns+CHr0KlUQFfTWRsbEEpG+wDHADTipBwVLVX8cdh+MMekJtdCoqq4HXgX2BzqJSCTg60uIK1r8jLQkG+XJRdHOdsuaEj4y4Wf0Kt5oVarzgxQ9qrl69epA2y56hZevVhX5XbuPiXHO+RNwFdCcyYVE5G8isspNAo8cmyQiS6NGk4720V4/EXlVROa6C3wudY93EZEXReQT99/OGbaXVh+DXoCUpL373FWQkf6N9NKeMYUmjFWE3dyRK0SkDfAtnKH8V4FI0buzgX/num/R/AQMqYKsIAKeRO16OSdR4JVpnzLJrQoyyFLVyapararV3bp1C6xdk5fWRH7X7mOHvX5E5FhglapOC+Ba9wFHxjn+R7ci+khV/a+P9hqBn6jqEGAM8GMRGYJTZf1lVd0DeNl9nkl76faxHhinqiOAkcCRIjIGuNFtb3ecLXTOy7A9gJ9G9W+Gx/aMKShhjGD1Al4VkZnAB8CLqvoscDVwhYh8CnQFpoTQt50EGQgEFWhl2kYm/fA7WpWqLROy6qJbkHcgMEFEFgGPAONE5B/pNKSqbwBfBdUxVV2uqtPdrzfh/GHZB6fEwv3uaZ4X+CRpL93+BboAKUl7xrQIYawinKmqo1R1uKoOU9Vfucc/V9X9VHV3VT1FVetTtZUrXgIKP1Nq6QY42RgJiyeXpRlshaEJkqpeo6p9VXUAcBrwiqqeGfBlLhKRme4UoqfpvFjibOw8CngP6KGqy92XVgCJtrfx2l7afcxkAZKX9lQ10r8b3P79UUR2LipoTBEoys2ew+I3MPEaMOUqsAqTBVmmQNwJ7IYz5bUcuNVvA+LsJfhP4DJV3Rj9mjqFCX2N8sRpL+0+qmqTuwVPX2A/fCxA8tKeiAwDrnHb3RfogjN7YUzRKejKvRub1qx5fu09ixO8XAWsCfByVaz10J73nOHg+1fo7a2Ne3QXPxeZNm3aGhFJdE+YeCTRdnV5yfP9oKqvAa8FeXFVXRn5WkTuAXzV2XKLK/8TeFBVn3QPrxSRXqq6XER64Yz2pN1epn1021gvIjssQHJHsdJagBTV3pGqeot7uF5E/g5c6bc9YwpBQQdYqpowo1lEaoKs+Gzt5Vd7iSS7J4zJVCQQcp9+G5id7PyY9wpObunHqvqHqJeexlnY83t8LPBJ1F66fRSRbsA2NxiKLEC6ka8XID2SqH8i8jcgssBgWLL2ROQ64FScJP1ORAWAInIjTokNgF+r6qPu8XHALUBrYBpwXtS0pTF5qaADLGOMyRYReRg4BKc0xBLgOuAQt6yAAouAC3w0eSDwPWCWm5cE8HOcwOoxETkPWAx8J8P2Tk+zj72A+0WkFCd95DFVfVZE5gKPiMhvgA+JvwDpPuAvwAMe2vsVTgmNEpwpzB4AInIMsA/O1GY58JqITAU24yTXj1fVBe77z07QD2PyhgVYxhgTh6qeHudw2h/qqvoWkGg+dnyA7fkpHRHd3kycRPnY45/j5GMle+8bbqJ9tFpgJdANqAMec8/dJ3KCiIzCCcwAhgBvuCNTje5K8yNxRtAaVHWBe96LOHlcFmCZvFbMSe6TU59i7RVwe8aY/DYZuFhVR+PkWd0R55zzgKnu1x/h1MqqFJEq4FCgH07uZpmIRFIMTnaPG5PXxFm0YowxxqTPHcF6VlWHuSsbVwPzo04pV9W9os4/E7gIODhSlkdErgVOcd+7CvhAVf8kIvsDN+FMHb4AHOuuTjQmb9kUoTHGmKCV4NTPGhnvRRE5DLiWqOAKQFVvwNlDEhF5CFjgHn8HGOsePxzYM5udNyYIxTxFaIwxJgRuPa6FInIKOCseRWSE+/Uo4G5ggqpuL0nhFiXt6n49HBiOM1qFiHR3/y3HqZt1Vw6/HWPSYlOExhhjMhK94hInsf064BWcoqe9cLbJeURVfyUiLwF746wgBPhCVSeISAUw3T22Ebgwsk+hiNyMUwaiBLhTVf+Ug2/LmIxYgGWMMT6IyMTYTaatvfDaMyZf2RShMcb4M9Hay6v2jMlLFmAZY4wxxgSsoKcIW7VuqxVt0trMfgfNZentxdbsYQ2mttr5WL+v1vJll67xzy/z9vsoKWv2dJ42K/ULV2x/Xj6wJ1Li//ttbswsFpfG9H7G9UuXrPGz/U3rskpt06ojza1L07peKqnulUT3RLz7IBWv90Im+q3+ii+7dUl6jtd7LVOty3be0FyblU2frI4+tFZVq7y2WVVVpQMGDMi8c1FWr15Nt27B7chk7WVm2rRpvv4fYUyuFHSZhoo2nakec3FGbdT1SOOTD6irSh0wbI3zn/yANat48Q+/57BTv8fiqm5sq/K3nVabqjpf5wMsveVxNr09h/YHDKXPlaf4fn+0LWsqM3q/X4snXuVr4+bytl3YJ8N7Ip5U90my+yHefRCP33shHj/3R885tbz6y5s55AffZVGPHTuZzn3m14CuX3k6b8akqax49RN6HroHK179ZJGvawwYQE2N9x3YTQEYPRqmTdv+1DZ3N/mqoAOsTKUTXPkNrKS5mTbbtm1/fuSnziKZoz6dxpQBY2m11Tm+pXUrtCT+KJGXD7vmLfWUtCmP+1qfK0+hJ2Oo6NoW2PlDbdHa5CMYifqSLNhq3lpPSUX8/hQSL/dIonsiVWAVG1Cl8zNLdW9E3xfS3Eyb+m00b22gpKI1357jBB4nzqnh/l3HbH/PlvJWqMfsAa9BUiZGTjqKxqvGM6rXGm4e+UnWr2fy3PTpqc8xJg+02ADLb3CV7ohVY1Uj5zz7Mhf99xXKmr+eavnJ0y/wk6dfoLGkhNuPGc/tx4wnekLIzwhC9AjVgb/beUuzGZOmMs8dARg56aidXo98SPoJtOL1MRJwrZ78D+pqZlJZPZxuE8/01WY+SXfUKllglWiUyuvPLN37os+VpyAKJ199Lz9dsnqH//AveeRVLnnkVRpLhDtPOZi7Tv4mySYncxFU7d1x2Y4HOmb9ksYYE6gWGWAFHVwl+kB1PkxL+NOEw3l78O78ccrD9Fm3YfvrSzt35LIffJfZ+/egnK2++gTOB11jXQPz3p4DwKa359BYN5ayytbbz2msa2DFq85f/Ste/YTGq8bv8Hpse+A/0IpoU1VH85Z66mpmAlBXM5PmswpvJCsbo1bJpv+at8b/mWUyTde8pZ5NUfdF85YJNAM/X7KaqcA/gP5R5y/r2oGrLj+JaUN2idtetoOqnQKqgGza2oiqIpJeDqDJQ716hd0DYzxpcQFWLoKreB+m7++5K/ePO5Cf//Prje4fPG4/Zu/fw3Nf4n3IlVW2juSm0PPQPXYKnlK9nuw66QRaJW3KaX/A0O0jJ237NgHZz+cJQq4Dq4iSinIqq4dTVzMzsJ/Zrn1r2Rj1e9+1by2L1nah/QFDefPtOUzepQe/Wbxy+/n/OOYbOwVXmQRV2QqY/Fq0tpZjb3+LHx2yO0cO60lpGgs8TJ5Zlh/3ljGpFPQqwvYd+6rXJPeg8628fqBGj0I8dM29DF64gn+N3ZtvvzGLubv24ozfnbdTG5G8GT8fcI11DUmDp8jr8T74Zm3onbTtdAKtZDlhfsw7cdI0Va32er6feyLC670R737wMx0YnWMVb3QqnZ9Z85Z6du1bm/D1ePdFY10Df77yX+z66RpeHL8nh780n0/37M6Vf/W/ACLXgdTNI5/wdT/sNmS47nLubXy+ppZdq9py4SG7ccLIPrQuswo1BWvSJOfhEhFf94QxudIi/i8TZHC1tVt6wVX3tRup3NrAqTeezwVbGqje1kjrL1fR7auN288Z0PUr1t/+IAvO+B3rb3/QV38TBVd7d1zG3h2XMarXmoQfhqk+JAd0/cr3aEYQwVUueB218hNcbatq3OleWD35H3x5yS/56r77E079efmZRX4X0ffKjElTE54f777oUdtAmy3buOyuU7nj6sO49O7TqKxroMuazSmvD1/fU7kOrsa0+9T3ezpXtubFKw7mjjP2oU3rUq56YiaH3Pwqf//fQrY07FwWwhSA668PuwfGeFL0U4RBB1fxJAusIsqamjn19+ezpbmZTW/PYQ6wb1093261krZdnff7yZdKJN0Pvcj7ko1mxQuy0s3XCls2Rq0STQdG51hF8qG8BlOJZHKvlDY1c9ldp9JQ7vznv3jXrlx216l0XL8l4XuyEUylEzClo7REOHrvXhw1rCevL1jNHa9+xvXPzOUvr3zKuQcN5Mwxu9CxTXrlWowxJpGiDrDyIbja/iHZFXqxCWB7bkynQ/dg3YCvC476yZcK4gNvTLtPeXfz7ju1m2rKMFqyICAfgy8/90QQwVXkfojOS4sXXPkdIUwnty5iVc8OOx1rKC9jdY/2Oxzzc4/lKljKhIhwyKDuHDKoOx8s+oo7Xv2Um5+fz12vfcb39t+Fcw8aSFW7whh5Ncbkv6LNwQoquPKTYxMdXKX6wEyWMzWo1Re0rkw/9m2oa0z4/ngfhLFBVoSfQCtbnjv4tsBysDIZtQLvCxogeY5VkCvykuXWZVumQdWW2ibatPVXcf+k3Wf4uh+qq6s1WaHR2Us3cOfrn/HfWctpXVrCafv24/xv7krfzrktqGt8mDbNKTbqshwsk6+KcgQr18FVwlGrJEb1WpPk1fR/LU9f9S7zX1jCoMP7MuEmp3hkqg/CyOuZjmblq2yMWiVbHZgox8pJRk+ckO5FPtSHCmK06tZLFvH2f9dzwNGd+MltAzLvVJqG9enIX7+7D5+v3sxdr3/GQ+9/wYPvfcGvTxjG6fv1T92AMcYkUHQBVraDK89Tgq5cjiw01DUy/4UlAMx/YQkjbp7va4Qg0ZQh5MdoVjrCHrWC9Mod5EuZg4h0g6oxFSt3OlZb28zb/10PwNv/Xc99f1xO27bx19u8u9V7GZNM7NqtHTedPILLDtuTyx6dwW//8zFHDO1Jl7b+ciBNDlRXQwHPvJiWo6gCrDCDqzADq4jWlWUccHSn7SMDyYKryAdf7AdYsYxmZTpqBd6Dq2QFQb0EV/kWTEV4DariBVHJtG1bwnHHVfDMM1s57riKhMFVOm1nqnenNtxwwjCO+NMb3PbyJ0yaMDSn1zfGFI+iCbCyta9ghJeVghGNdQ1pT90k+lBLlCcV+94xtw3gR79Lvvx8TMVKamubadu2hDEVK+OOEkSPZkVyugolyMplcBVPdK5Voly7fA2qIiL3YbI8qUTBT+TeSuauOztz6y2pzwvDHj3ac+q+/fjHu4v5/gEDGFDVNuwumRwQkR7Ab4HeqnqUiAwB9lfVKSF3zRSo/Pu/WxrSCa4SSbVBbyozJk3lpaPu4umr3vX93mQjBn6maO645kvOHDGLWy9ZFPf1C3+4jj0HreTCH65L2dbTV73Lnw/4V1rfT0u09JbHWXDG71h6y+Pb74Vkdary2a2XLEp6H8Xj597Kx+Aq4vLD9qR1WQk3Pz8/7K6YWNddl62W7wOeByJ/RS4ALsvWxUzxy9//wxWQyDRQdG2i+S8soaHO24jHmHafBrbMfUtt0w75LVtqm3Z6/ZlnnH0Pn3lmK7W1zbFNAM6IWWxOl9fvp1D4GcH0Inb/vx3qVNU1BHqtVCL3VLr3Var7KJ7a2mZP91Yh6N6hgvPH7sp/Zi1n+hepg0WTQ1FV3L0SkctFZI6IzBaRh0WkIs5pVar6GNAMoKqNgFWjNWnLeYAlIhUi8r6IfOTe8Ne7x+8TkYUiMsN9jMx13xLxOj0YqU0EMOjwvp5KLfj5APRybpu2pRxwdCeAuHlY0a+nyn9pXVnGoMP7At6/n5Yssg8jOHWvIvdCbJ2qbE8Pxt4n6QRaqe6jeCK5VZD63kpHrhLeIyZ+c1eq2pXz2/98TCGXsyk6vf2lKYhIH+ASoFpVhwGlwGlxTq0Vka6Auu8bA2zIrLOmJct5HSxxtrVvq6qbRaQV8BZwKXAh8KyqPuG1rfYd++qQ469Iuy/pLslPtXLQax2rdEYXkuViRbeXqsbQrhuX0r17WcIPrTdWDdj+PUTX1UqVg5VqT8RU58c+T6cOltd7Ioi9JmHn+yFVDlY6AVay2mbRUt1TsXl1qdqIvY8iz5Mln3vJwUp0bqL3Ru7ToOtgpfLge4u59qnZ3HXmaI4c1jPtdkyARHZYRZiqDpYbYL0LjAA2Av8CblPVF2LO2we4HRgGzAa6ASer6sygvwXTMuR8BEsdkU3PWrmPnP95GPT0ULRsBVd+3pcsuLr1kkWM2md1wjyZWy9ZtEPeldeRK785R7Hn50vOUqZ5eNGV2v1udRSP1zw4L/fGmHaf8r+fv+Q5ry76PvKak+U1uIrN10qUv5Xrkatop1b3Y/fu7bjxuXlsayrcKc+WTFWXArcAXwDLgQ2xwZV73nTgYOAA4AJgqAVXJhOh5GCJSKmIzABWAS+q6nvuSzeIyEwR+aOIxN2zQkQmikiNiNQ0NCVeyZeOTFaO+RHmtiLRuTXPPLM1bo5W5HU/eVc77Y2XIuco9vyta2vTzlmKvie2NXgr5JnNADtIXvPgvN5T6f5+Y3OyMs2vis3XWrWqMW7+VjrBVfT9sHr16oz6WVZaws+OHMzCNbU88v4XGbVlArLPPrFHqiK/b/cxMfpFEekMHA8MxElgbysiZ8Y2IiI/Btqp6hxVnQ20E5EfZeebMC1BKAGWqjap6kigL7CfiAwDrgEGA/sCXYCrE7x3sqpWq2p1WXnul09nWlg0iOAqkzZic7CS5Wj5ybuKzj/zsjde7PkVXdv6en+06HuiVev8XlLvd3rQSx6cn/shNrfqm90XpfW+TPOrYvO1uncvCyx/K/p+6NYtw+FIYPxe3fnGwC786aVP2LR1W8btmQxNmxZ7ZE3k9+0+Jse8fhiwUFVXq+o24EmcUapY56vq+sgTVV0HnB9gz00LE/pehCLy/4A6Vb0l6tghwJWqemyy97bt2k+HHXN5WtfNVv5V9AdobJ6Ln1GGeHkv0aJzpPy2H2nz0K5rPOVgxUqWhxXJOfKaixUvBwu+nlrLVg5WOvlX4K+Ke7wio34CrOj7J1HOVLrB9rrVDXTutuPvJ5KflazNyH0TLTLaFAmKMs3BirQV797MdQ5WxEdfruf4v/6Piw7dnSuPGJRxeyYDEyfC5K9jKA85WN8A/obzx/sWnHIMNap6e8x5s4Dh6n4oikgpMFNVrdqsSUsYqwi7iUgn9+s2wLeAeSLSyz0mwAk4SYYFKzZvxusHYWyeS7y8l9gcqXSkWhWW7orBssrWvnKpYoOw2Te9nBd5WGGLvX+CDK5uvWQRP9h/7k65VF5WG8beN5G8qUjulJ86WLBzvtZPrtzAnoNWcsrELZ7enysj+nXiuBG9ufetz1mxYWvY3WnZ7rnH1+luCsoTwHRgFs7nXuwoF8BzwKMiMl5ExgMPu8eMSUsYU4S9gFdFZCbwAU4O1rPAg+5fELOAKuA32epAEBv6JrPTnoDirVhhbJ7LutUNO9UiSjeHJlf85mIF9d6I5rLUuVX5nn/lJe8ql/WtEonOowIndyqTOljR7WXat2y46ohBNDfDrS9Y8dFCo6rXqepgVR2mqt9T1fo4p10NvAr80H28DFyVy36a4hLGKsKZqjpKVYe7N/uv3OPjVHVv99iZUSsN80ay7XGiRefNeK0hBDvnuXTu1nqnWkSx5+RbbSq/uVhBvbeYpMq7CioHz8+9GU90HhU4uVOZ5FFFt5dp37KhX5dKztp/F56YvoR5KzaG3R0TMFVtVtU7VfVk93G3quZXlG8KSug5WJlINwcrW/lX8HWOTWTUwWsScbR4OVhA3Lys6LpYQa5OTLX3YdD1sBK9128Olpd7ItUIVvS90FxfT0n51wta083BinxP6eZgRQRZ8T+dACZe/atMcrBivbu1R9K+hZWDFbG+roFv3vQqo/p35v5z9wusXePDsmU7FBtNlYPllYgcCEwCdsHZp1dwKgvtmmnbpmWyrXISSGd6MPLhGcmfeW5Sev9jj/1wibe3YOScMEo+eNnwOZPRp3wZuVrx8AN8fv01rHj4gYzaSbe+V7aCK0idg+dH27YlOwRUma4wzLeRq2idKltz8bg9eH3Bat76ZE3qN5jg7byKMChTgD8AB+EkxFe7/xqTlhYXYHkdvUpXbP5MpnkkXnJm0t1zLlk17mLldfSqub6ezbNmALB51gya6+vTCrpj88rSzZkLs3ZatDCLfuaL7+2/C306teG3//2Y5ubCnQEoWBMmZKvlDao6VVVXqerayCNbFzPFr0UFWLlIbk43/yqRIHNmoqUbXHkZvcpXfqYGS8rLabf3SADa7T2Spj6Jf+7Jpgdj88rSyZnLl+DKOCpalXLVkYOYu3wjT324NOzumOC8KiI3i8j+IrJP5BF2p0zhyq8M6QLSvCXeIhTHhJvG0DCpcXv+VbycEi+1riJ+ctsAfvQ7bzkzXq6VLLhKNzfHj0zys9KVLLhqaqhnW5+dNw7oefpZNJ94atLgyouRk46i8arxjOrlTCml2lcw1eu5+B1lKjYvK9l5yc7J1+/1uOG9uffNhdz6wnyOGd6Lilb510fj2zfcf6PzuRQYF0JfTBFoMSNYiT5g0ynPsPSWx1lwxu+S5tREPiAT1bFKVesqlpcPGS/XShZced1rLhNh7DeYLLj64j8P8PFfE+dZZRpcRUQCylT7Cqaqn5aL31GmYmtjpTov2Z6Y+fq9lpQIPz96L5Zt2Mrf/7co7O60LHffnZVmVfXQOA8LrkzaWkSAFcTUYGQaqHlLPZvengOkrtUUL3/KS62rdHi5VrK2Y8/NRn2tIOpc+ZVq5GrjghnA13lW2ZSqvlWq14OsYZWJZHlY8WpjxauHFbsXYew5+fK9JrP/bl0ZP7g7d7z6KV/VZv9eNq6JE1OfkwYR6SEiU0Rkqvt8iIicl5WLmRahRQRYQSppU077A5ydE1LVaoqXP+Wl1lU6vFwrdouTZO/PRn2tXNe5ShVYb+uzY55VdDkG8LaS1OsWOZC6vlXs67ElPrKVjxekeLWx4k0Bxu5FGHtOIXyvAD87ajC1DY3c9vInYXel5ZCs5dLeBzyPsyE0wALgsmxdzBS/oq+D5SexOSLeB2vsB2lPvqSi69cbCw9q9UXCfQf95GBFpvDSXa2V6FqJgqvY68SrrxUtiCR3PzlYmdTB8rrfYGytK/BepsNPgBVdIy36Xon3PFn9tHzIS0q1SMJvDlbk30T3Y0TYdbDiuebJmTxes4SXrjiYAVX5vdl4URCBqM+tAOtgfaCq+4rIh6o6yj02Q1VHZtq2aZmKegQrneDKi6W3PM5rJ07Znkc0Y9LUpHk18T4MY49FB1fgfICls9LPy7WixV4jFx/cuUhw9zMtnG5w5Ud0gdHoYCpeTlaq0cOwgysvYmtjJTsvWS5WIXyvlx+2J63LSrjp+Xlhd8VkplZEuuIktiMiY4AN4XbJFLKiDbDSzbtK9eEam4O1dW3t9ryiTPYGTBRMBVGrqqXVu8oksPYTXHndOimRRDlXhVCWIah6WLG5WPmYa5VK9w4VnD92V/47awUffuFtk2uTgWOPzVbLVwBPA7uJyP+AB4CLs3UxU/xyHmCJSIWIvC8iH4nIHBG53j0+UETeE5FPReRREcmPct4xYnOwKrq23Z5XFC+vxotUAVAmAVJLC66aU/z4gwquEkk0PRhPqpysMKTaIilosblYhTBiFc/539yVrm1b8/up8yjktIuC8MwzWWlWVacDBwMHABcAQ1V1ZlYuZlqEMP6PXg+MU9XNItIKeMtdtXEF8EdVfURE7gLOA+5M5wJeRq/8TA/GjlT0ufIU+rYZu32qa+Skoxh0w9CsBFfg/JU/pq3/3KxkbSerP5RoFCGkIqOB/REQZHCVzuhVvPpWkZppkeOxo1fZyreK126ug6uIu+7szK23RHKwdn49H3LOUmlXXsYl4/fguqfn8NqC1Rw6qHvYXSpexx0XaJAlIicmeGlPEUFVnwzsYqZFyfkIljo2u09buY9IMbcn3OP3Ayek034uqrXDznlE2Rp9iM1PCWJEKlnOS6T2UKJ8slxyc9xGBdFWkNsh+Q2u9u64LGn9q0T3TrbqQOVjfalEwX4+9jWR0/frT/8uldw4dZ5toZNNzz4bdIvHuY/zcPYjPMN93AucG/TFTMsRSg6WiJSKyAxgFfAi8BmwXlUjwwhLgD4J3jtRRGpEpKaxvjbQfmUjuTkZLyuxktUKSkeyNqNrD2WSTxaE6JpZqUTfE01bdr4nUgVX2f69p6pvFU+26kAlajes0atkCqEWVrTWZSVcecQg5q3YxL8/si108oWIDBKRGVGPjSJyWeR1VT1HVc/B+WN/iKqepKonAUPdY8akJZQAS1Wb3KWvfYH9gME+3jtZVatVtbqsfMcl0V5Hr4IczchEqum+eLWC/EwRvru1x07nJ6s/FF17KOycoOiaWalE3xOlbXZeJl+xOrh+pTM16CfXKhLoZKsOVLx28zG4gsKphRXt2L17MbR3B255fgH1jfkdELYUqjpfVUe6nzmjgTrgqTin9lPV5VHPVwL9c9BFU6RCr4MlIv8P2AJcDfRU1UYR2R+YpKpHJHtvbB2sTAOsRCMZXmodRS/DB38rwWJHsmJrA9XWNjOrtJfn9lJdJ7pGUbyAbUttEx/poLhtpMrDCnqfwecOvu1DVfW84WqbHv109zOu2Ol4LlYOeql/Bd6mk8e0+3T7iE02c7DiBVeZ3Lvpitzrr66tSlozLh/rYMV685PVfG/K+/zy2CGcd9DAnF67JfJTB0tEDgeuU9UD47z2F2AP4GH30KnAp6pqKwlNWsJYRdhNRDq5X7cBvgV8DLwKnOyedjbw71z2y09w5YWfUYHoICeSH7Xvfl/v5RZEcBW5zikTt7DnoJX85MrE5V0SBVepZGmfwcznRVNotSa7I3WRoPS5STVJ66VF+/mP1nDmiFnccc2XWenTRzoob0auou/5ePlWhTByFW3sHt04aPcq/vLKJ2zcui3s7hSfyZMzefdpfB1A7UBVLwLuBka4j8kWXJlMpBVguTlU6VbV6wW8KiIzgQ+AF1X1WZwRrCtE5FOgK06yYeCyNT2Y6Qq7d7f22CE/atkyJ64IsjZQdE7LM89s5dW1VYG0C+HsM+hHkNOE6fCThxV9bjZyj/IlsALi3vOZfs/ROXmrV4fzi7/6yMGsq9vGPW98Hsr1i9oFF8QeqYr8vt1H3M0K3dI/E4DHEzWtqk+q6uXuI940ojGepRVgqWoTMF9EfM9Pq+pMVR2lqsNVdZiq/so9/rmq7qequ6vqKarqa+fdXK0ezKZZpb2255x07eXkVmY7/yYoud5nsND4ycOKPTfI35OX4CqXAVh0TmDv3s7/jjK9N6Nz8rp1Cyfhcu++HTluRG/ufXMhqzbGqT1hgrQm8vt2H4mGuI4Cpqtq3HltETlRRD4RkQ1uIvwmEdmYvW6bYpd2DpaIvIGzhP59YPvSLVWdEEzXUvO671yE39wrSDxFGJ1vE8k7is3DSrWnXKxIrknsv4nOS1eq3J5kH7DxRuqi866ykIPlK+cmUQ4WBLOaMPp+aN5ST0mbr7fZSVZkNNE+hMlE52xF50WtW91A527xf8aJ7g2/QZPXPCyvOVjJ6q5Fvx4vBytaIeRgRSxaU8thf3idU/ftxw3f3juUPhSlNPciFJFHgOdV9e8JXv8UOE5VPw6sr6ZFyyQH65fAscCvgFujHi1KoryjSN2jn/8o/ibLsaLr/UQ+YOJ90ARRF+iOa74MrLZQ7PefzyNXQU4TLr3lcRac8TuW3pJwtiEuPyszo3O23t28O+9u3p3vHzifH+w/l4lj5+x0fqJ7I+wpwWR11yIiwVeiTckL0YCqtnz3G/155IMv+Xz15tRvMN48/bTvt4hIW5x832RFQ1dacGWClHaApaqvA4uAVu7XHwDTA+pX4NLJvUqV4B6bd/ThcienKTbf5o1VA5K247XeTxB1gVK1kerDOHqULh/zrkpyULorej/KTW/PoXmLr9lsT+LlbG1es5VNK7cAsHb5Nl5a1Hd74JWv9a2yUcutkFw8bg/Ky0q45YX5YXeleIwe7fstqlqrql1VNdnmzTXuNm2nu9OFJyap8m5MSmkHWCJyPk7l9bvdQ32AfwXQJ98yyb/KpMhkoryjePk2yT7ovOZGBZFDFUQbkSCr2PKuvK4mjN6Psv0BQ3eYJgxKvHuoXVUF7Xu0ca7bow3tqiq2n/+RDtrh/HxZJZis7lpL0K19+faNoGd8uT7s7hSHPnFrUAehA06NrMP5urp71naWNsUvkxysGThFQt9T1VHusVmqmrNkg0gOVqoAK90aSF5rHkXnHUWP8MTLt4nUN8oktyqIvdmCyNWJ5GMFnXcVzW8OVtuu/XTXs+PnYEVkUhMrWQ5W3zYrEv4cYvPzvIp3D21es3WH4CrV+enIdQ5WtGTFdAspBytic30jB9/0Knv0aMfD549BpPAX5IQqzRwsY3Itkz8n61V1+5yQiJTh7ClYMILaIiXRh2q8D7pIfaN4+U9eg6Z4m/RGHl4FvYIwn1Suyc5tGBtwRwdXS295PBs1wOLeQ4mCq0Tn54OWNnIVrV15GReP2513P/+K1xeEXC/EJCQie4rIyyIy230+XER+EXa/TOHK5P96r4vIz4E2IvItnNoiwW1xHpB0616lW2A0WT2soOsbxQuq/AZase/1I91RmWITnZOVKBct0zppprB99xu70K9LG258br5tBJ2p88/PVsv3ANcA28ApKYRTmNSYtGQSYP0MWA3MAi4A/quq1wbSKx8Kqf5VkPWNUgVD6YxqtRTJVhOmU9U9OierGHLRwHuw7WdvzJasdVkJVx4+iI+Xb+Tpj+wPk4xkVsk9mUpVfT/mWHg73puCl8l8wsWq+mecqB8AEbnUPZYzlWs07SCr1ZqyhNOEW9ZUAvFHslKtGvtweRWjesVfbj7hpjE0TGp0E9/97fsWkerDLzYPJ/b8ETJ/e3AXec15T+prt4SRmET3xZY1lQlHNvtceQrNWyawa9/auK/D1z87vyN/QeVV+RW5N1Ldo+9u7RG3tlqi/KzYfKzI+wttSxy/jhvem8lvfM4tL8znqL17Ul5W3N9v1oweDdOmZaPlNSKyG26qi4icDCxP/hZjEstkBOvsOMe+n0F7aUuWc5Oq9lGqEYtIoBURqX/0v2tejnt+pC5Usv3mkgU/yXgZkYrU30p0/aevepczR8zi5z9as72tVO8BJzgopOAqW3lYsfdDtJI25Sxa24VFa7skbSPys/TyM/Xyu8m2VPdcpP5WbG7hu1t77PSI7IV5ysQt248FUdutEJSUCD87ajBL1m3hwXe/CLs7hWt61qoB/RhnVfxgEVkKXAZcmK2LmeLnO8Bya4Q8AwwUkaejHq8CiUtZh8hLkJUs0Ip8qMbWP/p8SdsdzouuC5Vqvzm/vARiqfa7i/d6svd4DQIKUSaBd7IgKyJVkBUtUcDlZ//CbEsU3EfX3wL/ddyCqO1WSMbu0Y0Dd+/K7bYRdN5xt2s7DOgGDFbVg1R1cdj9MoUrnRGst3Eqts9jxwruPwGOSPVmEeknIq+KyFwRmSMil7rHJ4nIUhGZ4T6O9tOpIEYsUn2oxqt/FP1BGlsXav42b1s1esmn8iLVfnfxXo93LMygKjIC5GUkKNtyGWRFi/z852/rv/1+SrV/Ya7E3ovRddXAfx03r3XZiimf0DaCzlCvXllpVkS6ishtwJvAayLyZxHpmpWLmRYh7TpYaV9QpBfQS1Wni0h7YBpwAvAdYLOq3uK1rei9CCOS5WN5XVGYqjZWqj3oYutCec25iZfr4vdDJXr/umTnxL7eUNfoOSBMR7xaWZEAJPbnGW3eiZN818Hyuj9lUPeDF8n2KUwlWZ0xP/lcyXK50snzir5fU+1vGS1evlXssTdWDYjbn5tHPlFwdbDiueih6bz88Spev+oQurdPXHbDpBZUHSwReRF4A/iHe+gM4BB3VMsY39L+k9jdQuBGoDsg7kNVtUOy96nqctzEQVXdJCIf41SBz7qK1d4+VFMlv7ep2vHYorVddvgAjf0wnLWht6cPwnc3777Dh5bf4Orpq95l/gtLGHR4XybcNCbhebEfXF5Hq9IdkVl6y+NsensO7Q8YSp8rT/H8WrYFdz+kDrJi7xE/kq1K9Pq7mzFpKite/YSeh+7ByElHJX3Na9AWnQTvJ0E9Xh03ANzt+pz7eEbK+7iQXXn4IJ6bvYLbXv6E35xgG0H7MmmS8wheL1X9ddTz34jIqdm4kGkZMplzuIkMdx4XkQHAKOA94EDgIhE5C6gBfqKqO+0OKyITgYkArdt23qnNVKsK/XyogvdipJl8gEZLdxpkp3ydSclHJPxMAWYyVbfzvn0Tto9WJXvNj1T3RLYlW3EaLfJzDOI+8WOnPSOvGr89aIv32iz8rXYMcurO731cqCIbQT/43hece+BAdu3WLuwuFY7rr89WgPWCiJwGPOY+Pxl4PhsXMi1DJqsIM9p5XETaAf8ELlPVjcCdwG7ASJwRrlvjvU9VJ6tqtapWl5W3jXdKynysVAnO0eLl4STKv0kWiGQ7pylV/lWkD37yq4LIg0q2b19Qe/p5uScy5aU+1pY1lZ5zs3KZY5Zsz8hkr4WRi+flPi4WF4/bg4qyEn43dV7YXSlY9Y2BLoo4H3gIaADqgUeAC0Rkk4hsDPJCpmXIZC/CPwM9cTZ43l4YSlWf9PDeVsCzwPOq+oc4rw8AnlXVYcnaiZeDFS2oHByIP5IVb8Qi0R6FEV5HBdKtfRT7vnQ+ILPxwZ8szyqsHKyIdO6D5q31lFQkDgj97gTgZ1Qr3b0fk73PS5u5rNyf6P4vlhysiL+++ik3Pz+fh87/BgfsVpX6DWaHvQjPu+8D/nbOfrYXoclLmfx5GL3zeIQCSQMscXY6nQJ8HB1ciUgvNz8L4NvA7Az65klkJCvdPJx4+TeRqcJEOS9e8rG85lLFM39bf9jg6y3bZXNEJdnoVLojV6lkUoQ2kVZrylj25H3U1cyksno43SaeGfc8r9OGEdE/+2TBVrJcqlSSBVBeArZ0C6WmI9M/EgrFeQcN5KH3vuDXz37MsxcfRGlJ4exMERo3YN5c38ibn8Qv6JwO97PpDGCgqv5aRPrh5GXFVnc3xpO0AyxVPSfNtx4IfA+YJSIz3GM/B04XkZE4QdoinO13MuL1AzaTZOd4QdbnS9omzHmB5EFWOjkomX4AeQ2s4k1/pbtnY7rXC1tzfT11NTMBqKuZSfNZyUey/AZakDhXK1kuVS55XbTht82WqKJVKdccPZiLHvqQx2u+5LT9sreSt9i8Pn81DU3Nns4VkU7AvcAwnM+Yc1X1nZjT7gCagXHAr3GWXfwV2DegLpsWJpNVhHvi5E31UNVhIjIcmKCqv0n2PlV9C2fFYaz/ptuXZPwEWZA60PISZEVyiza9PSfh3nSJPqQiOSiREaxsBleZBFbxXgsi2AojqPIaYAOUlJfTbu+RbJ41g8rq4UmDq2heVxtGix3ViuRLRUawwtzz0O9oVksNoLw4Zu9e3LfLIm55YQHHDO9F+4pWYXcpv1VXgyrPz1lBl7at8VgJ9M/Ac6p6soi0BuL9j+YbqrqPiHwIoKrr3HONSUsmU4T3AD/F2VoAVZ0pIg8BSQOsIJU0Bl/Dy8+HbTJ9rjyFvm3GplxiH/mAis45id6vMJl0P7SCCKyiRfKRYs/3ElDk4yhVtOb6ekrKdwyiep5+Fs0nnkpTH397yaUzmhUR+Z11uvgMOvyg3i1yG9PXLfVJ90KMbSsRP/lgFjhlTkT45bFDOP6v/+OO1z7j6iMHh92lvNfQ2Myr81Zx1N49+TDFuSLSEfgm7lZuqtqAk8gea5uIlPL1XoTdcEa0jElLJgFWpaq+70xbb5eXO4/7zcVJFWR5nSpcsqUnAyqTf1jN2tCbhTc8uVPOVVAlFqIFHVgBrJ78j4T5SPECilwHVJlME694+AE2z5pBu71H0vP0s3Z4raS8nJI13st4RMsk0IL4OWuRemLLA6gnFlY5iZZsRL9OnDiqD1PeWsh39+tPvy75/YdH2N7+bA2b6hs5YmhPbk59+kBgNfB3ERmBU9z6UlWN/WvkNuApoLuI3IBTpuEXgXbctCiZlGnIi53HK1d628/L71Y66exVFy94SBXUNNY1eNpvLpN9Af2UBPATADVvjclH2lof97xI+YKwRqu8/u6jf+fN9fVsnjUDgM2zZtBcH/97S7WPZTJB/Ux2ricWv69+5cN2RekQkYkiUiMiNatX+6jJErKfHjmIEoHfW9mG5K67jhfmrqRt61IO3L0KoCry+3YfE2PeUQbsA9ypqqOAWuBnsc2q6oPAVcDvcD7LTlDVx7P6vZiilkmAVfQ7j/upl5VMsg+q2DpE8aqs5yrPysuHfSSgaLWmjJKKciqrhwP4ykcKg98gK5JrBdBu75E7TRPGSjfIgsxH9YKqJ5ZIvgRaXvsQXRetW7cA5vtzpFfHNlx48G78Z9ZyPlhko4eJNP+/63hx7koOGdSdilalAGsiv2/3MTnmLUuAJar6nvv8CZyACwAR6RJ5AKuAh3HqYa10jxmTloz3IhSRtkCJqm6KOX62qt6fUeMptO/YV6vHXAxAXQ9viaHxpouaGuopbZ34QynZdKHX+lgRiaZdMq1RFMvPB6LXwCqR+na1OwRXqWpE+bF44lVp18FKxMuUYeR3Hi8HK5l0pgyjxdvr0qt0a435la2pw+h7NlF//dZFy/c6WLHqGhoZd8vrdO9Qzr9+dCAlVrZhJw09erLnOVP482kjOX5kH097EYrIm8APVHW+iEwC2qrqT93XFuLMxAjQH1jnft0J+EJVB2bx2zFFLOMyyXHmsSMuBbIaYAXhi/88wMYFM+iw50j6H3NW6jdkKNGWOokCqHTqHgU9HZhqdKZ8c9vt+8h5qREVxDUz4Scnz09wBcn3LfTii98+SV3NzLT2ZkwUQAW932MmOVpe7s0w96cMW2XrMq4+ahCXP/oRT324lJNG9w27S3mn9aqVtCoVDh3c3c/bLgYedFcFfg5sLzMUCaBE5B7gKVX9r/v8KOCEoPptWp5MpghTycs/vaKnipoa6tm4YAYAGxfMoKkhfu5KsqlCP1vpRHidctmp7lFdvIUv/tv10kfwn1+0U42oBDlZya6VSU6TH0FupxQr3f5H57QFlUuVrfwsSH6/RW8H5GdroGz2t1AcP6IPI/p25Kbn51HXkJfrhkITmXHZf7cqOvgoZ6GqM9zpw+GqekK8fW6BMZHgyn3PVOCATPtsWq5sBljB11BIwmuyO3z94VraupwOe44EoMOeI5NOE/r9wPW6J10yyfaJi20nG7lWfsXmLZVvbhu3ndhgKsiAyk/pjso1mjTQyjTI8vt9xea01dd2znl+VjrXSyeQSiTb+WSFoKTEKduwcmM9d73+edjdySsLVm5mVo/dOGJoj2w0v0xEfiEiA9zHtUDu9ocyRSfjHKyEDYt86K7YyJroHCzwnocFO+bhpMrBiggyFytWvKrdkYAqXg5Wqg+x6ByWyNfZCqx2urbPvKVk53/68yt85dykk5cHyfOyMq2LFntvpMpRS/a6n3sq3j0QLdX9EETh2ES5VKlywlpqDla0ix6azksfr+SVnxxC705twu5OXrjt5U/440sLeO/n4+nevgLAUw6WF25C+3U4NbMUeAP4laraigOTlkwquZcDJwEDottR1V+5X/4vo56loXLlNs8fqtF5OF6CK/BfhNRP9e7o3KzYvKuyyta+RgWic1jAmWrxkhMV1EiSn+AqWa2pTKV7P8TKtPhsdF5WsrphEV623okVe5/F5jF5DbDjXSvdQCtRLpWXHKuWOHIV62dHDeaFuSu56bl5/Om0rP6tWjCen7OCu9+8m+7tjwm8bTeQujTwhk2LlckU4b+B43GKi9ZGPQBQ1Ysy61r2+a2NlUyi4MTPh9qitV12yrv6fElbX8FVbA5L5OtkOVFep7MqVu/8yITXWlOZSGfqOJ5Mv9dWa8o81w1LR3StsdolpTvcA7VLSjOaakynXleiXCrLsfKub+dKzh87kH/NWMaML9eH3Z3QLVlXx5xlGzn8f8+E3RVjPMkkwOqrqqeq6k2qemvkkepNItJPRF4VkbkiMkdELnWPdxGRF0XkE/ffzul0ys8HKgQbZCXi5wNqyZaeGeWgROewVFYPT1mnyuuoVaIAI5Ngy2+tqXRVrtzm+76IJ9Mgq3xz25zUDctWfTI/93GiXKpEx6MDxGSPluaHh+xOt/bl/OqZOWQrnaNQvDBnZdhdMMaXtHOwRGQycLuqzvL5vl5AL1WdLiLtcbYtOAFnn6ivVPX3IvIzoLOqXp2srdgcrAg/uTfb3+NjKx2/uVjRvE63NK7bRFnn9nFf81LTqHZJ6fYP1ng5PZkGVqn4mVKL5GDFu9bsP6afgxWPl3sj1b0Q/b35zTeLfk+mNbNSXscdIctWIOflXk6U/5durTS/ddEKOQcr4rEPvuSqf87kttNHMWFEy9378dS732F93Taev+JgiPrcCioHy5igZZJ0cxDwfbdIWz1OWQZV1eHJ3qSqy3G31FHVTSLyMdAHZ7rxEPe0+4HXgKQBViHykteSLEclVf5KpP2Siq+P5Tq4in1vvGBrx7Zzl2/jJS/La52sdPPHIgFZ5PeQrUBr7QOPB1KTLBFve01WRiUOfC2fq/7nm5NG9+W+txdx49R5HD6kR6R6eYuydnM9Hyz6ih8fujssXRpo2yJyO0lWvavqJYFe0LQYmUwRHgXsARwOHAcc6/7rmYgMAEYB7wE93OALYAUQdx1u9D5j2xri1zhNZzrIz1Sh37pY8SSa8kiWo5IqfyWoVYJB5FfFay+o3K1YXu6JaF6mDFPlYwWZP5aNchXZzPeK1ZKn8HKh1C3bsHT9Fu59s2WWbXh53iqaFY4Y2hOmTQu6+RqcmZRED2PSkvb/0VV1cSYXFpF2wD+By1R1o8jXIwaqqiIS9xPO3WdqMjjTQYnaj3yA+pkujHyoehm9SLayzE8179hRgEiOSmSUKnoqMN5rQVVjjwg6+MkFr/dErFSjWclGsio3OjXUNi6YEWj+WOzvKd3RrUgeVmQEK9GIUbL7IttTmMa7/XfryhFDe3DHa5/xnep+dO9QkfpNReSFOSvo06kNQ3t3gL4TdpgizFS2t3QzLVf2S2bHISKtcIKrB1X1SffwShHpparL3TytVUFcy89S/e3v8ThFlCrIAu8fUtGBVp8rT6F5y4QdEoAjunz/bDqd5uSvbFnjqemiDq4ylUmQ1f+Ys2j61qlOmQ/3Z5dpzaxY8X53Xu+pbhPPpPms9PPvsj2Fafy55qi9eGXe69z8/HxuPmVE2N3Jmdr6Rt74ZA1nfKM/0X+IB01EuuGkpQwBtkewqjouaxc1RS2bldzjEue/kCnAx6r6h6iXngbOdr8+G6cMRCDSWUWWqsp3RKopLz9TP81b67dPtUSqeMcboYo3GpGsBEMq2Zi2C0tJQ5Pv92QyXRhbQy1b06DR/FbCz7RqfibTl9mcmmxpBlS15ZwDB/LE9CXMXroh7O7kzOsLVtPQ2MzhQ3pm+1IPAh8DA4HrgUXAB9m+qCleOQ+wgAOB7wHjRGSG+zga+D3wLRH5BDjMfR6odAMtL1J9oKb6kFo9+R98eckvWT35H366l/C9fmpb+REJPHNR3iJd5cs2+n5PJkFWIrkItiLiBV1rb3+ILy/5JSsefiDQa3iVyT1t4rto3O50rmzNr56d22LKNrwwZwWdK1ux7wC3cs/dd2frUl1VdQqwTVVfV9VzARu9MmnL+RShqr5F4o2gx+eiD36nDYOYMoyIN+2yU0JynGmdROK9t3xzW0/v9fPBHy/A8PpzCUP5so3U9+7g6z2ZTBemEvuzDnoqMdZOSfgnnhp4nliyqcPY+7L0mKas1TlrSTpUtOLyb+3JL/81mxfmrnSSvotYQ2MzL89bxZFDe1JW6o4HTJyYrctF/spaLiLH4OxDmP7GmqbFC2MEKy9kc8rQi+jRgEwKQ8a+N1fBVfRr+TqalS8jWfFke2VlLoq4xhvRihwr39w2J0VkW6LT9+3H7t3b8fup82hobA67O1n17udr2bS1ccdAMnt5WL8RkY7AT4ArgXuBy7N1MVP8QklyD0pJQ1NaIxUR6a40TDWK4WfvusgHVKKEZC+6TTzT8whBUIFVonPzbUQrEmT5uUeyOZKVTNCjXD1PP8vTyFW8e8LPtRNNG3q9vvGnrLSEa4/ei3Pu+4B/vLuYcw8aGHaXsuaFuSuobF3KQXtUZf1aqvqs++UG4NCsX9AUvYIOsCLS+RCNlo0pQ78bBDsfUmVsq9h52sVT3kt5sPlW6Y7UBBl8BDky5jcQDyvIipZp4AOJN95OdS9EXs80yLPgKjsOGdSNg3av4s8vf8KJ+/ShU2XrsLsUuOZm5YU5Kzl4z24ZF1cVkUXAJqAJaIyu/C4iV6nqTYkKjlqhUZOuogiwIjIJtNIJsiD5iE06H1JBFpuM7YcXQQQ16Y5mZXuqMRtBVqxcB11+AyC/U5FBBVomWCLCtcfsxdG3vcntr3zKL48dEnaXAjdjyXpWbarfOc/s2GPTbfJQVY1X3OZj99/C3lPJ5J2iCrAi0g20sjllGJHrD6pcB1fx2ktYrDOE3C2/90a6gXdEPgRcQeR3WaCVf/bq1YFTq/vxwDuL+N6YXRhQ5S3/slA8P2cFZSXCoYO77/jCM88Eeh1VjTRYp6qPR78mIjvvR2aMR0Wd5F6+bGNWEp13Ot/nNju5WLbv9zrZDHaiSzvkS5kHP/dFOlsvbX9vjr/nbCfPB9VeMdVeC9MVh+9Jq9ISfj91XthdCZSqMz24/25d6dgm5g+c43ztyLa9SeAFEZkmIomWIV7j8ZgxnhTlCFasdBOdwftolt9psWyOCORLYJXv/EwZprMjQNx2QphWDJrfezfZ/RhEnlks9wN0IkD//v0zayzPdW9fwQ8P3o1bX1zAe5+v5Ru7dg27S4H4dNVmFq6pjZ/A/+yzsUeqRCR6em+yu31WtINUdamIdAdeFJF5qvoGgIgcBRwN9BGR26Le0wGwbQxM2lpEgBWRjRVlO53vM/k5yOnDdIqGtnRhBFk7tZsHQVc6U5vxAq0gpyOj+flvI3pvyurq6qK/yX8wdlcefO8Lbvjvx/zrRwdSUlJYAXs8z89ZAcDhQ3p4OX1NdNJ6PKq61P13lYg8BewHvOG+vAwn/2oCO27uvAkr02Ay0KICrIhc5eH4/ZDM1ZSJBVY7yocga6fr5CDoSlXfzOs1czXlbeJr07qUq44cxBWPfcS/P1rKt0f1DbtLGXt+zkpG9e9EjwA2tRaRtkCJqm5yvz4c+FXkdVX9SERmA0fYxs8mSEWdg5WK3zycoIuTNjXkdp+2IPKAIj+HTPKS8pHXe6GxsT607z2IPDa/77VgvDCcMLIPe/fpyE3PzWdLGvtx5pNl67cwa+mGxHsP+t8iqAfwloh8BLwP/EdVn9uxSW0C+olI8dW7MKFpkSNY0dJZug/+VxpGREYEvvjPA2xcMIMOe46k/zFneW7Lr0DKLiQIKKKP52JUJ2xzPnqI1Stn0q3HcAYefnbqN+SAl6m9jIPqPC0ia75WUuKUbTht8rtMeetzLhq3R9hdStsL7vTgEUMTTA9OnuxruxxV/RwY4eHUhcD/RORpoDbq/X/wfDFjooQygiUifxORVe6wbOTYJBFZGrMBdE6ku9IwnZGMyjVK+fKtbFwwA4CNC2ZkZSQryNEqP+cW8shWsvugsbGe1SudvfVWr5xJ66Wbd/ie/TyyKZurNfNh9adJbMyuXTl8SA/ueO0zVm3aGnZ30vb8nJXs0b0du3ZrF/+ECy7I1qU/A57F+VxsH/UwJi1hjWDdB/wFeCDm+B9V9Zbcdyfz2lngfRSntFU5XXYZwVeLP6LLLiNov7E1dQHtBBFEUJVxH2LaSPRzycdgLNGIZllZOd16DN8+glVWln6F8njfdyGNAGYyopUPCf3F7Jqj9+Jbf3idP764gN+dODzs7vi2rraB9xd9xQ8P3i3n11bV63N+UVPUQgmwVPUNERkQxrVTydXehrt/8yyattVT2sr5oM50GiaTwMpLoNPYWJ92UJGPgVQyie6BoSO+S2PjSRkFV4kk+hnlc+CVbMVsOvtYgqf7v0XnjaYysKotZ+0/gPveXsjZBwxgcM/0/l8WlpfnraKpWTk80fRgFolIN+AqYCiwPbteVcflvDOmKORbDtZFInIWzpLZn6jqujA6kUmQBd4DrUhwtcN7czgF4zXwic49Gjriu1nuVX7LRnCVTC5GuzK5Rraq/8POwdYX/3kAYFSgFyxCl4zfnX9OX8IN//mYB87dD5HCGSF8fs4KenesYO8+HROf9PTT2br8g8CjwLHAhcDZgK1fNWnLp78G7wR2A0YCy4Fb450kIhNFpEZEahqa6rLWmXTysmLlY16S3z7F5h41NuZ25aMX2bgngvj9Z0umOV1e3p8P9210HllTQ/32vEWTXKfK1lwyfg/e/GQNry0onPigrqGRNxas5vChPZMHhaNHZ6sLXVV1CrBNVV9X1XMBG70yacubAEtVV6pqk6o2A/fgFIKLd95kVa1W1erWpZVZ7VO6W+3EE3awle61I7lHQMa5R9mSrXsin4OsaKkCpkwCsrCDrIj2G1vTZRcvC8EM4OxN2LWS3/7nYxqbmsPujidvLFhNfWNz6unBPn2y1YXIzb5cRI4RkVFAl2xdzBS/vAmwRKRX1NNvA7MTnZtrQX/Qxvuwa9qWhZWEAQV1Q0d8l4PGXd/ipwcj0h3FiwTs2Q7cgl61mA+jWeDkLQIfht2PQtC6rISfHbUXn6zazCMffBl2dzx5Yc5KOlW2Yr8BocU0vxGR/9/eecdZUZ19/PvssruwVGnSxS6IgII92I0NW2ISjb2hxl5iNCbR5I1J1CSvGhMVo0ETY0kxMb6xV0xUBF1QAbGgKB2RpSxsfd4/Zu4ye/eWKWfunbt7vp/P/ezO3Jkz586cO/O7z3lKb+BK4Crg99hM7pYIFMUHS0QeAg7AqSH1OXA9cICIjMcpyvkJEFssbhi8D8Uo/lnpVC9vbPVx6rvVuNRDJFJ7cdB9RT2QW1iYPC9JId0fL6g/WjYxlW19ks9hmBxwMbRVGuaYBHDYzluyx8i+/O+zCzh2/BB6dk1ewETtxkY+W13HZ6vreG7ecg4dPYgu5YX93S8iXXF8rrYDhgL3quqBBe2EpUNSrCjCkzKsvrfgHQlJ+sMxykPR6+O0+tPZVC5en4hpuDBWlkz7ZDs3pTL9BptFVnt/tMwRhVE+W5BzWCwCF0LPIfpNijZLW0SEH0wexTF3/IffvfQR3zt8p4L3oaGphSVrNvLZl3UsWu28Pl+9sfX/2o2bx0aZwPG7+pj+O/dc0928H2d6cDpwBDAauNT0QZKMiPwe+LWqzhWR76vqz4rdp45A0qIIS5IoD0WT+ZWiEofoKSUhlYuqJWthSK+s1yrOz5lU0ZVJHIW1oHa2qgCFYuywPhy/61DufXUh395jBMP7xuu3ur6+iYdnLOL5eStYtLqOpbUbafEEmlaWlzFsi24M71vN+OF9GNG3muF9neURfav9WdmmTjXd7dGquguAiNyLU06nU6Gq53gWvw9YgWWADimwmpob6FJe3JJSQRKXxplfyS8dRQjFTaZrVYxz52d8RcldFgTT09KFKqjdWfjuYTvy73eWcvPT7/Obk+LJcvHF+nru/+8n3P/ap9RubGTM0F7sPnILRvQd2iqeRvSrZsueXSkri5g2YsIEmDXLTMcdWgewqjYlNa2FmzvySeBVYB9gMXCsqm7MsO22wG+BAUAdcC7wIfAa8F1VfUlEfg60qOp1IvISjt/ZCUA3EakB3lPVk+P+XB2ZDiewZi96jGVr5zGo1yjGjTi+2N3xnVMrCdOCltykrmWSrlW28RU2d1nUHHCmsCLLHEP6dGPKftvwmxc+5MMV6zlu/BCOHjeEIX26RW778y/r+P30hTz85iI2NbZw2M5bcv7+27LriC0M9DwLb71lusVxIpL6lSQ4AmOt+7+qavG/EJvZHjhJVc8VkUeBrwN/yrDdVOB8Vf1ARPYEfqeqB4nIGcBfReRi4HBgT+9OqnqNiFykquNj/RSdhA4lsJqaG1i2dh4Ay9bOY+fmo4puySoFOpT1qrEp1ubTBUgSzl16n/z6iqW3kTSsyDLHJQdvT/8eVTz29mJ+/uR8fvHUfPYY2Zfjdh3KkWMG07s62HlesHwdd738EY/XLAEc36nz9t+G7QaWXuk+VS0vdh8CsFBVa9z/ZwEj0zcQkR44Fq6/eKxxVQCq+p6I/BGn5uLeqtoQd4c7Mx1KYHUpr2RQr1GtFqykiKukWAUsZkhdzySJEu8Y8+vXlyvC0Y7XjkVFeRmn7zOS0/cZySerNvDPmiX8s2Yx1/79Ha7/53scsOMAjh0/lINHDaRrRXa9MevTL7nzpY94bt5yulWUc9reIzln0tZGrGG+GTw4/zYdF28odzOQ6cSXAWtyWKF2AdYAA432zNKODiWwAMaNOD6Rliv70Cogny+HYYWvZVZsvGMsl19fkoRhPqwVyzwj+3fn0kO255KDt+PdxWv5R81i/jV7Cc/MXU7Pqi4cNmYQx40fyt7b9qO8TFBVXl6wkjtf+og3Fq6mT3UFlx2yPafvPZItuhfhPrtkSeGPWUKo6loRWSgi31DVv4hjxhqrqrNF5Gs4yVP3A54QkT1UdU1aE40iUqGqxU9+V+KUtsBqbHIepl6GbZk4cZVkSulhmySSet7SLVnp75UiVmTFg4iwy7De7DKsN98/chSvffQF/6xZzFPvLuOvsz5nYM8qDh8ziJmffMncpWsZ3LsrP5w8mhN3H073qiI+Om64wXkFQETKcWrcLlbVyTH0KmmcDNwpIj8AKoCHRWQx8AvgYFX9TETuAG7DqbnoZSowR0Tesk7u0RDVwhUXNk3vLgN0757HBt+xiNaNpFmxkv7Qffrdn81S1Yl+t28zJjqhFSuFCT+xJI3VlMCa8ccrA42HiRMn6syZM2PrV0dkU2MzL8xfwT/eXsxL769keN9unL//thw7fiiVXRJQ/EMEPM8tEck7JkTkCmAi0KuTCCxLAihtC1ZYMli9LJaORNKFc1CsFatwdK0o58hdBnPkLoOpb2qmoqwsemqFIiIiw4CjgBuBK4rcHUsnIgE/RxLA58vbvmLE1IMvbD28TkXM17KQNDUXPtgnaSItCfUQOxtVXcpLWly53ApcTQcssyQivxWRmrTXmcXul8XBCqxMxCy4oj643pv9Z1594Xrem/3novbDUhhmL3qM5+f9ktmLHit2V0oCEZkiIjNFZObKlSuL3R2LadpP+fZPXW/3NSX1hohMBlaoqq/MpCJyn4isEJF3PetuEZH5IjJHRB4TkT5hu56pfXf9xe4x3hORm/22p6oXqur41As4GjhNROa6bV3qtt9XRJ4VkQ/cv6ESlYnIcBF5Mb19z/tXioiKSH+T7YvIeBF53RWQM0VkjzDtFxorsPxQAMuWX9rnOLKWrJwk5LqFJT23W6EtWaUowlV1qqpOVNWJAwYMKHZ3LPGzKnW93Ze3ls6+wDEi8gnwMHCQiGRKzJliGk4CTi/PAmNUdSywALg2Ql/btS8iBwLHAuNUdWfglxHabwKuVNXRwF7AhSIyGrgGeF5Vtweed5dNto+IDAe+CiyKof83Az92ReSP3OXEYwVWEAwKrbAPrlSOI6DotQst8ZPK7QYkKrebxVI0JvqOcUBVr1XVYao6EjgReEFVT8mx/SvA6rR1z6hqKoPx68CwwH3O0T5wAfALVa13t1kRof2lqvqW+/86YB4wFEfA3e9udj9wnOH2Af4XZyo2dORcjvYVSEXd9AZKIldH53Ryj4qhPEthc2OZqF1YipaJ0CQlL1ZKnAfsS7Fzu9kcbhZLK2cBjxhucwdgkojcCGwCrlLVN6M26tYu3BV4A9hSVZe6by0DIt8Qve2LyLE4KTBmm6rlmNb/y4CnReSXOIahfYwcJGasBSssRZ56sparzoW1XFks0VDVl6KkaBCR63CmsB401yvAMXT0xZkS+y7wqERUKW65nL8Bl6lqm1/T6uRmipSfyds+zjn5Ps7UnREy9P8C4HJVHQ5cDtxr6lhxUtJ5sERkJfBplrf7A6sMHs62V5z2tlJV3440dkx0+PZMjgdLxyDQmMiHazl5QlXHeNadAZyHk6SzzmT7IvIUcJOqvugufwTspaqhIjREpAKn1uDTqvprd937wAGqulREBgMvqeqOJtoXkV1w/LpS52UYzhTeHqq6zFD/a4E+qqqu+KxNWBHujJT0FGGuL5WIzAySkDAftr1ktZcNOyY6b3uZMPngtXRORORwHN+i/aOKqyz8AzgQeFFEdgAqCflDxhUf9wLzUuLE5XGcjO2/cP/+01T7qvoOnrqGbkDBROBmTxTnmAzNedu9AjgHxxo2APi3R1zdhFOs+mMRuQbn3HwgIgfhBARU4hS+PtvjK5cI7BShxWKxWCyAiDwEvAbsKCKfi8jZwB1AT+BZN03AXYbbvw/Yxk3d8DBwuoafWtoXOBUnWjKVF+tIHGF1qIh8ABziLptsPxPTaB+RmY23cUTZd4BBwNfdtj8GDgUOwylQ/QfgJhxr4v3Aia54+5T2JX+KTklbsCwWi8ViMYWqnpRhtTF/nyztA2SNbAzY/qtANv+tg2NuP7XNSPffV9zp0FZEZFvgtzhWqjrgXFWdn5oeBV4Vkd2AO1R1XxH5LtBVVV8GdhWRe4GncQRVg6oucPd7Fid9RqJ8szqyBWtq/k1seyXcXhiS/plsexaLpSMzFbhYVScAVwG/y7DN2cCT7v+zgcNFpNpNXnogMBxnmrCLiKRcDE5w1yeKknZyt1gsFovFkky8Dv1uZOBK4H3PJlWqOsqz/SnARTj+bvXuuuuAb7j7rgDeVNVbRWRvnISjVcAzwGQ3EWlisFOEFovFYrFY4qYMWJNNBInIIcB1eMQVgKreiFOoGxH5M042fVT1NWCSu/6rOPnEEkVHniK0WCwWi8WSANx8VgtF5BvgRCSKyDj3/12Bu4FjvJnsRaRcRPq5/48FxuJYqxCRge7fKuB7QOjgg7iwU4QWi8VisViM4kZMHoCT7245cD3wAnAnMBioAB5W1Z+IyHPALkAq2/wiVT1GRLoCb7nr1gLnq2qN2/4twGQcQ9GdqnprAT5WIKzAslgsFoslCyIyJa2AtG2/gO0X6hhxYKcILRaLxWLJzhTbflHbL9QxjGMFlsVisVgsFothSnqKsLKsq3Yr6xly54pAm7dUBtOiLRWCqrLxi8Wt67r1G4qpSuPpmDhWWWM8Y6GsoaXNcpBzub528aog5U58j4mA1z8Xfj+PqrJh7ZLW5e69hiAitFTEMybyket6x92nfOO1JUt886ZlnwcbD12qtVtVn9D9jIvmyvJA27f4GK5+tsmFVgT7/mtzM42fby41VzFsEFIe7HNFQRqd8VK/JNiY6N+/v44cOdL3cVauXMmAAfFVXLLte1iyBIYMiXyMWbNmBRoTcVHSaRq6lfVk717Hhdt5xOBAm28c7l/IrR+0+bR+/PwDrFlYQ5+tx7PNwacFOmZQTByrx7JElXLilSeuDlSo1/eYCHj9cxFkbMyb9SdWLp3DgMFjGTXhlDZjpbORbbxuHJh9n3k/uyLYeKjqw16jzwvdx7jYMLx7oO3XD80vXOoG5W+nflBjoOPmY/F3f0bzl7WUb9GbwTdcbrTtfFQtcxTlBz8INiZGjhzJzJkzY+mTJSIijsiK3IwkosB757y7xyiu0tnm4NNobvwW5RVVodvIR+qBNPik09hqcbzH6hAUSVwBjJpwCjs01VPexV6jTN+NXOLKkjyG3vJ9mmrX0qV3rzbrtxi0ji+Xhb9vWiwdgc4psGIkk0UiLsGT6WHUMNQ5VrcV7d/zw/pBXRJnxepopMRVZ7ZepUh9N6ywih/T1qsUmcSV969fggqy+kGNrVYsSwdhsLkfv0nAOrmXIBsH5n8g+dkmG/bB748olk3LZqy46hhsMWhdYFGVvr+lk2NgejBJWIFVQoQRTal9gu5nRVa82PPrYMVVfvz4XxWTqMIqvS1LJ+aGG4rdA6PYu7xB4npomnoIpdrxO31opwuzY61XDlYgJQM/Du5xUGxBFNe0p6VI/PjHHUpkWYGVh2I9SON8cAUVWhazdBTrlRVX4QgaQWgC00IkTmFlHeQtHYWCTxGKyHAReVFE5orIeyJyqbt+vIi8LiI1IjJTRPaIpQMGI8i8mHxoFurB5ec4HUUMmKSzW6+i+PdZShuT04EWS0enGD5YTcCVqjoa2Au4UERGAzcDP1bV8cCP3OVORTEeXFZkBSOquCr1c2mFVeek0MLKirhOSgfLT1ZwgaWqS1X1Lff/dcA8YCigQCretzdQ8uEEzY31vrfN9eBqafDfjl+8bVqRlUyCjJ+4ySf+TY3ROMa6JRpRxU7D6vVtlps3NkRqz2IpFYr61BSRkcCuwBvAZcDTIvJLHOG3j/EDxpRgNJP4CJJVPdeD6/PHHmDdvBp6jhrPsOPNZILP1ObGgfl9sjq703shpwYLWQEgH/kEuKkxGsdYt4T3vzJhRZp7xu00rlpLRf9ejJ52CZ/c9Hdqp8+l96TRjPze1/Ie3/pidTImToQSLt+XTtHSNIhID+BvwGWquha4ALhcVYcDlwP3ZtlviuujNbNBNxWuwwFobqxnzcIaANYsrMlqifBjFVg3z2ln3bwaI7/u42iz2AQaEzH54PnBrxXQ7/jJhDctR9QpZz/7mxpPJsdlm/HQVBe6nSRgqkROUEyIq4bV62lctRaAxlVr2bRkNbXT5wJQO32uL0uWnSq0lDJFEVgiUoEjrh5U1b+7q08HUv//Bcjo5K6qU1V1oqpOrJSu8Xc2BOUVVfTZejwAfbYe3y6Te59N6309+Moqq+g5ymmn56jxlFVGzwifq02/U4VJmy4sxJgopPUq3/hp7ZNPMZVruz6b1mfdxw+mxqjJsd5mPHSpDt1OZ8WUqKns24OK/o7XR0X/XnQd0pfek0YD0HvSaMq7VYbr37oNRvpnscRNwZ+UIiI41ql5qvprz1tLgP2Bl4CDgA8K3TeT5KpB+N3XHuOKEaf6amfY8afRctS3jIgrP236mSqEzNaYjjh9aEpYBRWl2caPyZxo3VY4Y/G6A/2NxWyYGqNxjPWkUegUDUGnB01bjEZPu4SG1eup7NsDgJHf+xrNl0wOLa4AfvzQ41wy5SRTXbQkieuvL3YP2iAiWwI/A4ao6hFuQN7eqppxhi2dYliw9gVOBQ5yUzLUiMiRwLnAr0RkNs4HmlKEvhklk7gaUbuCwz96m61Wr/TdThwPnFxtRimx430Vq42sFLDIt5ewnyO9CLLpCL7UWBxe638sZsPUGO3I4ioohZwejDNKMCWuUkQRV1svW8mxM2oYuXxV1G5ZkkjykoxOA54GhrjLC3D8xX1RcAuWqr4KSJa3JxSyL4VAtIWuTZt/RX714xoAjpj7Ng/ssX/r+o0VFagUv3JRS0M9ZZVVRpKRJm0qMQgmxJWJz59JVKWuUVBEW+jW2H4sHvbx2zw4ZvNY3NSlAltFyzxJtV5FFVbNGxsiiaZcSItSvWmzP97Rb84G4JgZNfz+0ElULa9gsR2sHYchQwpSj1BELgfOwcle8A5wpmpGB97+qvqoiFwLoKpNItLs9zil+wQMw6KlBXdyFoVT57zI2TXP0UVbWtdf8spTXPLKUzRJGXd95VDu2vdQNJvsLBBhowtLAp/XvdhWKy+ZxFWUSDtROPP1FznvP23H4gWznuKCWc5Y/P2uh3Lv+EOjdt0SkUJZr6KKqyBRgWEQVaY8/QoX/98LdGnZPGav/OezXPnPZ2kqK2Nr2NL4gS3FYenS2A8hIkOBS4DRqrpRRB4FTsSxVqWzQUT64QgxRGQvoNbvsazyz0G3z6KbzFvKypg64XDOP/IClnbv3ea9JT17c+bJ3+F3kw6jpczMpdg0UAO9WvuZI4qrsySXTLq4ihpp11JWxm/3O5wzT76AJT3bjsWl3Xtz3lHf4Z7dzI1Fy2aSaL0yYbkKGhUYlJbyMm499lBOuupcFm/Rdswu3qI3p531HT6H+J/Klo5GF6CbiHQBqsmed/MK4HFgWxH5D/AAcLHfg9g7aYF4e/C2PLLzpDbrHtx9P2aN2MZI++mCKch+kD+Kq6RFVh7r1cbhPY1NCcYlrsBcpN2sEdvy593bjsVHdt6PmkFmxqIlGoWwXpnwtyrvVpkxKnDcgCWMG2B2mmfGDttw/8H7tlk37ZB9mTnSjtkOxW67xX4IVV0M/BJYhCPOa1X1mSzbvoUTfLcPcB6ws6rO8XssK7AKyIGfvMvGLhU8NG5PNnXpwsEL3jHSbhhhlWn/Ycefxo5X/Tzr1FNJi6yYMeVvlu8c57tGfjn4fWcsPrrDnmwq78IBn5gZi5ZomBBX+axXJp3ZR37va4z5y9WM/N7X2gkr0yLrsLffZWNlBX/8yu5squjC4W/M9b2vNzfaypXRgzosMTFrlqmW+qeut/tqDZoTkS2AY4GtcZzXu4vIKZkaEZELgR6q+p6qvgv0EJHv+O2EFVgFYsCGNXTVer51xmX8z1Hf4ptnXE73+noGrPM9nZuRqOIqvZ18VpGSK/Trw3oVlUKJqxRRI+0GrltDdYMzFm844VucduzldG+sp/+GaGPRkplCTw/mIo5Iwd1GrMoqpkyJrC2/rKV6UwPHXHcRPzjjBI7+wcV0r69n4Fp/Y9abG23AgAFG+mSJgSnGkgesSl1v9zXV894hwEJVXamqjTj5N7NVjjlXVdekFlT1S5yMB76wAqtAlLe0cOIZl/LhQOeB/+HAwZx4xqVtHDfjoqW+va9OpnVBxJqpbOGxEnNAg8lUEoU8h13SxuJHfQdz+rGXtnF8N4HXR6wjVAyIm7itV9nEVRTfqXEDltBYl9tiZmLKsEtLC8dedxELhjonYMHQQXzj/EspL8D901JA7rmnEEdZBOwlItVuXs6DcWoiZ6Lc3QYAESkHfIfMdq4oQihKJCHAsp59qa9ou66+opKlvcOHN/sRRCumPcCGmtl0Hz+OgWeclnWdt82uK4KHM2YSCEmPPoxivTKZgqLQAnVJ777t1tV3qWRZD3Oh9t5oR8BIjUFT1tpCUkjrVZiag2GjAFOC6eXrXuTT5xay1SFbs/+NB+bdZ/bKITm3ycbiflu0W1dfUcnSPvGkh7B0XFT1DRH5K/AW0AS8DUzNsvlTwCMicre7fJ67zhfWglUgTD9E/TxsWurr2VDj5I3ZUDOblvr6jOsytW3iYVZUK1dMU4OmE6Am1voXgfRoRxM1BktRXAUhbsf2TNarsFGAKXHVWNfIp88tBODT5xbmtWR597VYiomqXq+qO6nqGFU9VVWz3Zi+B7yIUyv5AuB54Gq/x7ECqwTx+7Apq6qi+/hxAHQfP46yqqqM66Iexy8lMa2YA9OJU5NyDkz0wztW0qMdo0Q+mhL7xcCk9SqqY3smskUBZiN9qq+iuoKtDtkagK0O2ZqK6opsu7ZrJypVy/wdy1JiLF5c7B60QVVbVPVOVT3Bfd2tqjbRqCm6fbauoIV+8xH0YTPwjNNoqa9vI6Qyrct1vDBThn6IbVrRsPUqjoz0SRFXcZFeVzBMjUEjwqo+uPAoNH6sV1HI5djupzZgLkG0/40H0njdV3yLK2+bfqYLv1yWnHuvpQDMmuVkc08IIrIvcAOwFY5eEkBV1Vd+ECuwCkCxH6aZhJQfcVUMOkzm+BwUezyYJpsQ8gqqotUYrCp9S0cc1isvUcvcBBFXYX2wLJ2EY44BTZTF+l7gcmAW4NtylcIKrJA0N9VT3iX/Q8Pkw7RUp0qCUAxxletalnI9xUITp7Wz1PA7PVhM65Uf/EznNdY15hVZVlhZSpRaVX0y7M726RGCebP+xMqlcxgweCyjJmTMT2YJQTHEVaGvZUezXsVJ1xXSKX5UlDK5ogjjElXW/8pSQF4UkVtwcmW1OsK7Gd7zYgVWQJqb6lm51MmUv3LpHHZoqqcQp9E+aMyT6Vr6sUpaLIUi7unBXOSzXrWLInR9sUwJK+t/1Qm5++782xSWPd2/Ez3rFDjIz85WYAWkvEsVAwaPbbV6bByWfSrAlLWiM4grY9arSv+/btOvZdziylqvOgdJytweJ6kowpQFy6S4snRSzGVyN4Kq5k7uloeCCywRGY5TkXpLHCU4VVVv87x/JU4hxgGquqrQ/fPDqAmntFo71he7MzFTCJ+aYjq1e69lnHQWcWX9sPyTZP8rv6kUvFGEVlxZIiMSycldVXl8trlcayKyJfAzYIiqHiEio4G9VfVeP/sXIw9WE3Clqo4G9gIudDudEl9fxUlln2gKNZXU0a1XSYgYtNOCycWKtezENT0YNE+VFVeWJLBi7SbOmvYmlz5cY7LZacDTOEWhARYAl/ndueACS1WXphzEVHUdTg2goe7b/4uTJbWkVEVzY/sksMW2WGTK0O7nvcjHDZCpOwniKg7Sx0PYsdDSUN/ha/h19M8XhSiZ24OQnsHdT0Z2L0HFVfrxgtRCtA7ulkz8+52lHHbrK/z3oy+44ejRJpvur6qPAi0AqtpEgHQNRfXBEpGRwK7AGyJyLLBYVWd7aismno+ff4A1C2vos/V4tjk4fJ21TIS1XuWqNZjrvah4689FqTmXFMKkaDA1HlLnEugw5zOdjjZeIDn+V36nB9NrEa659UEe8llbEIKLq/TjZauFaB3cOymTJwfavLaukesff5d/1Cxh3LDe/Oqb49luYA/ONNejDSLSD9foIyJ7AbV+dy5aqRwR6QH8Dcfc1gR8H/iRj/2miMhMEZnZoJvi7WQempvqWbOwBoA1C2syWrIKTa5ag37qEIY+blr9uXyWCZPWqzZjoqnOXMMBaW5sPx7CWK+85xKi1fArBr7qZAYYL0GnCZMyHnIR1f/KxPRgei3ChtXrA9UWDGO5Sj9emFqIlg7Mv/7le9NXP1jFYbe+wr/mLOXyQ3bgbxfsw3YDe5ju0RXA48C2IvIfHP/xi/3uXBSBJSIVOOLqQVX9O7AtsDUwW0Q+AYYBb4lIOyO5qk5V1YmqOrFSuhakv90+y/xrsLxLFX22Hg9An63HU15hzpcnrPUqV63BIHUIAx83rf5crszdpqcG24yJLtVmGw9AeUXb8dAwNNz59Z5LCFfDL+kEGS9BScp4SDrptQh333Gt79qCYXyu0o9X2bdHoFqIlk7A0Ufn3WRjQzM3PP4ep9z7Bt2rynnsO/tw6SHb06XcvJxx3Zn2B/YBzgN2VtU5fvcXLXBaenHm/+4HVqvqZVm2+QSYmC+KsHeXAbp3r+OCdyJPrbpMZKtft35QF5ob69uIq6j+VyYc23PVGvRbhxCCWw9aGuqNiqu3fn/FLFWdmH9Lh97VQ3TvHc7JuY3fWoRhs7inxkPUcZCy6hRCXKVflyB9zzRe/Y6bfOMl/RgLL7sy2HjoPkT3Gn2e380jEWR6MJ8FK0r+q6DRg80bGyjvVtnq3J4vM3tUh/bU8bItQ+Ypwmz+Vx/8INg9YuLEiTpz5ky/m1sKSZ4owprP1nDFIzV8vGoDZ+47ku8dvhNdK9p/l0Qk0JjIsP/Xcr3vGobyUgwfrH2BU4F3RKTGXfd9Vf13EfpiBJOWK1PkElBxiSsoYs25BGFCXEFpnMuoPwb8fkab1X0zpqMHveIKctcWNBEtmC6m/IgrS+emsbmF37zwIb998UO27FnFn8/Zk3226x/nIVOmtIE41qsX3OUDgf/iZHbPS8EFlqq+ilOROtc2IwvTm2jks3D4/XXuJUkPkY6Y08iv9Sof6VbLNsfoJDmvcmF67CTpe5EJk9aroLRsqqesa3gxHjQtg8VSSD5Yvo7LH63h3cVr+dpuQ7n+6J3p3S18NKmI7Ag84lm1DfAjVb01tUJVz3S3fQYYrapL3eXBOKkbfNH5MrkbnB7MuK37cA0aIZX0B4gp0sVHKaZqyBUpWGxxlT6O/IqcjQM3Xwu/nyHfmA3Tl87yPchFrunBdOvVqrsepG7mHKonjqX/+Sf7mh6MIqhS+8aZ92qLQesyWrHqBzXaNA0dHc/0YEuL8of/fsJNT82ne2U5d568G0fsEvz53f4Q+j4wHkBEyoHFwGNZNh+eElcuy4ERfo/VuQSWYd+rbLSLkDrqW1ktWUl/oMRtxUqq4Mp2fdtHCn6r1ZJVaHHlZ+wEuX5R/a7i2KcUKJb1qmVTPXUzHX/buplz6NX7cCC7o7hJS1UhhJalEzJ1KkyZwierNnD13+YwY+FqDt5pID//+i4M7BlLUNvBwEeq+mmW958XkaeBh9zlbwHP+W288wismC1XXlIRUikLViZxVUoPm0JOFcYpUkxMD6YiBVMWrEKKq7BjprNN13VU0q1XZV2rqJ44lrqZc/JG4cU1DRiX0MpmxbJ0cM47j3t2OoRfPfs+FeVl3Pz1sXxj4jBizI15IpvFUztU9SLX4X2Su2qqqmazdrWjcwisAokr70N22PGnZbRc2YdT6bPNwacVzHJlaryk2okitOzYbY9p61XQ7O39zz+ZXr0PL4q4ynSMpFq0RGQKMAVgxAjfMzyWAvLB8nVsD9z473kcMmogPz1uFwb1Dm216i8i3lDRqao61buBiFQCxwDX5mrIjRj05dSeTscXWCHEVT78hu97xVViHk4D05KzrvA3gDuiw3sU4hRXcY6VsNcxMeO3k5ItctDxuSquuMp0vLiEVlg/LPfhOhWcNA2m+2UJT2NzC3e//BG3P/8hC4DbThzPMeOGRLVarfKRpuEI4C1VXZ5tA9d6dRNONKG4L1XVXn460bEFVkhxFdV6lU6mh1OQXFRBaamvp2y4+XtIkIdzkAjKMNGWQQlyTf0KaNPiKm4RkxpzmwYqLfX1VNf6F9dhjxXX9kmgmJGDkD3fVdC8VlHJ1L5X2KW/71d82WnCjs+7i2u5+q9zmLt0LUeNHczaR/7GseOH5t/RDCeRY3rQ5WbgaFWdF+YAkQSWiPxRVU/Nt64oxCSugiafzPRwylsPMN3KFIAVtz/MhtffofteuzDwkhPz7zBwk28rVjrZHrx+6x12XSEdsh5dEAplGfJeE6D1/xFHnp5zvzD9C1rvMs76mB2BTNarbOIqVdtvq0O2hhsP5OXrXuTTALUFg5Kv/UzvZ7OqJXV60WKe+qZmfvP8h9z58kf07V7JXadM4PAxg2BJYSqci0h34FCc7Oy5WB5WXEH0Ujk7exfckMcJEdssGmGdoLNZMrJZrnLWA4wgrlo21bPh9Xectl9/h5ZNPuvXBTjmpoHa+srYhwD1Dut6b2oTbVnXe1Ob9pM8LWUiW3+hPl/6NfH+nzrnkfo3cFPrq6VXbaB6l3HWx0wKfq1XQf2v0vHW+vv0uYXUfVEXqLZgUBrrGnO2n+/9dGw+rs7B24u+ZPLtr3LHix9y3PihPHv5fo64AhhaGOuVqm5Q1X6qmq9w80wReUREThKRr6Vefo8TSmCJyLUisg4YKyJr3dc6YAXwzzBtGiUmp/awpVO8ZK0HmHpIRWm7axXd99rFaXuvXSIlHwzdhwD1DuOsjejFVHJRExRDOKaf50zn3NunQP1LG7PtxuBwzTmuCzUGTBNkejAKQaxX3lp/Wx2yNdX9qn3XFkwxqc+CjK9MVFRX5Gw/3/t+yPRZTWeytxSGjQ3N3Ph/c/n6nf9lQ30Tfzhzd371zXH0qU50DcpeQB3wVZzs7kcDk/3uHKkWoYj8XFVzeuDHScZahDH6XWUSWEGsV17a+JxEFFbt2g6b2TnkVGHGPgTwqcm1beDacxlqEQYVWH6EdBgLVrEtct7zHNnnKc+YDToGWz4TX/1JSi1CvwIriO9VJgtWEIEFjhUo3ecpfTmbYPLD9DU7tFuXz8criA9YpmnCfHUJbS3CZNHQ1MKG+ibW1zexoaGJDfVNLKut55an5/PJF3WcvOcIrjliJ3p2zTAm8tQi9EvUWoSmCGWSEZGdVHU+8BcR2S39fbcCdeEpsLiKQlziCghvuYrgj9WuDwEe3nFaLTqtuMpwLb3nOU5xBcHHoBOUYf67EAdxWK9MiStoX0swtRxFWKVIteEVWvnEUxDL1bgBS6wvVoJZsmYjv3vpQ75Y3+AIqPom6hqaW//fUN9MQ3NLxn1H9K3moXP3Yu9t+2U/wLnnxtTzcIjIDsCdwJaqOkZExgLHqOpP/ewfVjVcgZNT5FcZ3lPgoJDthqfA4ipXLTpfD9O0h1TKX8rPgylq7TGLGfxGPwayZmZYzrd9K+nCJ7Vs0DIZxw+CjkockYPZyOe/lEtc1a5soPcA/9M0mzY0M6nPglaRFXeUoo0mTAYb6ps4a9qbLFy1geF9q+le1YUeVeX071FFj6oudHdfParKPf9v/rvzkF50rcjznZg6Nff7hece4LvA3QCqOkdE/gzEJ7BUdYr713xYShgKLK5StehCR76lPaRSkX9A3ui/wFGCYfpm8oEc9Nglgt/ox3ziKj2CLl9EXbv3/ZwzU0KrhK5PXCTR9yqKuLp6/xl8uayBLQZVcvPLe+Tt192XzWfmk6uYeER/zrt183JcUYrZsHUJC0tLi3LZIzUsWL6OaWfuwX47DIjnQBMmwKxZ8bQdjmpVnZGWk6vJ786RoghF5H/cyMHUci8R+UOUNgMTQyLRXHhr0a2bV0NLQ8CopwyWq5S4gtzRf6GjBJOIJ/LMhIN/JuJwbt84MEOtySxjwI/lyhtB17R2bc6IunYRd73yBcCkEeUcW3EViKi+V+mEFVe5qF3ZwJfLGgD4clkDtSsbcm6/aUMzM59cBcDMJ1dRu7KhddlklKKNJkwev3r2fZ6du5wfTh4dn7gCeKs43kU5WCUi2+LMzCEiJwBLc++ymahpGroAM0RkrIgcCrwJJEp+RiGT9SpViw7IWGcw50M1w0PKG3UFuaP/ChYlGOfDNCYxZYIgGfp7jhoPZB4DfkmPoOvSq1fOiLqyKgPXP8z5T+j1SiqmHdtz+V1FofeASrYYVOkeozLvNGHX7uVMPKI/ABOP6E/vAZWty2GjBC3J5581i/ntix9x0h7DOWOfkcXuTqG5EGd6cCcRWQxcBpzvd+dIUYQAInIw8ATwJbCfqn6YZ/vhwAPAljiqcKqq3iYifYFHgJHAJ8A3VfXLXG317jJA9x5zQei+h00qmrJiRBVXXlLWqGHDN7auW7KyT9ZtY/fBimOaMMSDeuG3rwsdRRi3g3suH6wgTu2+fLA8587Y9fdzjRMmrgKPB4NRhH6mB6PmvArr2J6LfM7tYXywunYvb7P8ZuMo3/v7Id3RPVckoY0ijI+az9bwzbtfY/zwPvzp7D2p7BLVJpOHIUNgSXQLpukoQjcxaZmqBvq1E3WKcD/gduAnwEvAb0QkXwhIE3Clqo4G9gIuFJHRwDXA86q6PfC8u5ybyvC/mKJOHwWyWviMuvKKK4AhA9YwZMCajNuWHAl7UKcTJnrQVHmfTJaqNmTINWWEfNYsn9csNU6zjdfOQhLFlR+CiCugjbjKtGzpGCyr3cSUB2YysGcVd50yIX5xBUbElUlEpJ+I3A5MB14SkdtEJEcYZFuinrFfAt9Q1Z+r6rdxPO5fyLWDqi5NpXFw1eA8YChwLHC/u9n9wHER+xaJXNarTGTL2h7kIZXrPZMPLl++WyEFUUt9faBs3HH5kRUzuaixlAyp7OgBzlEu/72sy5mudYRxG0ZsJd2fMJ/1Kk5x1bwxt29UPjZtaA70fr7to2I6o7zFPBsbmpnyx5lsqG/i3tN3p2/3AiUDveGGwhzHPw8DK4GvAye4/z/id+eoyZ32VtXWb6Oq/l1EXva7s4iMBHYF3sDJM5FyHluGM4VYsrRGe/mI9vP7IEptl23q0Fe/YoxCTH1moG0UXJaHdewRkQYxXdg5J57zFeQcZds2fX3G7UIIaj/jNn2bTGO3lMZBFMKIq1Rtwd6TRjPye5srdPi1Xr183Ys88NxCN+pvp3bvt40K3KndsmnC1ka0qRoKh6ry3b/O5p3Ftdxz6kR2HFTA8/7jHydNZA1W1f/xLP9URL7ld+eoFqxtReR5EXkXwE3C5cspSkR6AH8DLlPVtd731HEMy2gGEJEpIjJTRGY2NNVF630Wolqv2kR75Yn2C2OZCmvRijMK0fuZIX9dOZN9iTomTCaRjWy9SvO18nuOsm2bvr5pzToj5z2sRTV9OnHLHiuMj8lC3CO8+LFehbVcpWoL1k6fG9iS5a0FOPPJVRktVdmiAjNtnws/SUyD1ia0FIc7XviQJ+Ys5XuH78Qho0vazmGCZ0TkRBEpc1/fBJ72u3NUgXUPcC3QCE4SLiDvT1ARqcARVw+q6t/d1ctFZLD7/mCcuobtUNWpqjpRVSdWdqmO2P3oZHqg+o32ijrtF3QqJnAUYgCrhjciDjxRcFnaMBkRmT4mklR70DcZ/KGCnKNs26av79KnZ+TzbnK6urxbJf323xGAfvvvyLDhG9uJsKDj3PQ9Imruq7A+V97agr0njaa8mzNN49d65a0FOPGI/hl9p7JFBWbaPiomahNa4uWpd5fyq2cX8LVdh3LeftsUuztJ4Fzgz0ADUI8zZXieiKwTkbU59yR6LcI3VXV3EXlbVXd119Wo6vgc+wiOj9VqVb3Ms/4W4AtV/YWIXAP0VdWrcx0/U905v2R7CJvwvUo9KHNFexXTGfjzz7r5f7AGjCZMWa38lgLKFxEXJopw/MGX+93caGmcUNYrHyI2SNRgtm3T14eNRIxr3DZvbGgVELn4z6G3FDyKMJfAyme9MuHQ7j03YRzbd6+Yl1MsZYoKDCOuMtUpzISfzO9+ahLaKEKzvLeklhPufI2dBvfkoXP3yp91PQ5mzXKSjUakpGsRegiThGtf4FTgHRGpcdd9H/gF8KiInA18CnwzYt9iJ98DNYniClKpIDZG8uXKRpsIuBhq1nUofFoIg5yjXDnUwraZIs5x60dcJY1c4ipX8tCg0YJRz00+sVToqEBruUoeK9fVc+79M+lTXcHdp04ojrhKIK5B6GRga1X9HzfN1GBVneFn/6gC60JgKpuTcC10O5MVVX0VkCxvHxyxP74wZb3KSI6HZtgHVPqvez+/9v1sM2TAGiMiK1/tvELSUul/1tuE71UqF1agvFe9anMKnHzWpWLUoiz2j4JiEWZ6MKi46tX7CyD79zu1HFeG87AWq2Jjy+WYob6pmfP+OJMv6xr5y/l7M7BnkUqlAUycCBFzc/pBRPoAvwfG4BiIzlLV1zJs+jugBae+8v8A64HfArv7OU6kJ4yqfgwcki0Jl4icrqr3Z967tAk6HRT2ATX/p4/zxcvv02//HdnpB8e0W/azTyTy1CbMWjsv4Xmv/JJLXHvrEfY/71Rf7a14eFrOiLl8EXXFiLjrrOIqF9msV0HFVe3997PIEyWYHjWYWt7qkK0hhlp/cUcNWpKNqnLt39/hrUVr+N3JuzFmaO9id6lQ3AY8paoniEglkM1Zc09V3U1E3gZQ1S/d7X1hJHOYqm7IkuH0UhPtFxMT1qsolqsvXn4fgC9efp+G1evbLGeKKkrfJ1/kUZSHZ7vaeAHyXxWbqNardvUIfXz2lvrcUYH5ogb9RBWGdQzPhp/9x/Zd0vrqSGSzXgUVV/WDGrNarrxRgg2r12ddjiPqLj2KMGr+Kz+RhJZkcc/0j/n7W4u57JDtOXKXwtb1LRYi0hvYD7gXQFUbVHVNls0b3XrLKTeoATgWLV/EnZo121Rg4gjywDWWSDID3gdVeoRVZd8ebZYzTQGm7xOnb0t6LT2/ju1JoMcy3wXRM+KtR5ipbmDGffJEl+aLGvQTVWjSr86vuEpf7mhCKy7SowQr+/bIuhxH1F16FGHUaUK/Tu6WZPD8vOX8/Mn5HLXLYC45aPtid8fh+usLcZStcRKG/kFE3haR37uzcJm4HXgMGCgiNwKvAj/ze6DItQhzNi7ylqruFlf7YaIITfhfZRVYBixY3ofTnNVOJE1cPlgp8j6U80QS5qudF4WgUYQ9+wzT3Sb5M5yaiCAM7IPlZmaP2wfLhH9dGIHlJTV+TVLIKEKTFqxMePNd5fp+N29sYLcRq/x0OSP5LEsmfLBMiys/UYTgRBL6iSIUkSnAFIARI0ZM+PTTTw31tDR54+MvOP0PM9huYA/+ct4+dKssPR+8XIjIp4D3SzNVVae6700EXgf2VdU3ROQ2YK2q/jBLWzvh+IcLTjm/eX77YS1YIei6orAfK10o+RFOhYzKSoqDezEIU48wnziK+j6YtWSFpTNZsoKKKy/5vt9Rv8v5xE/SxFUceHOjDRgwoNjdKSpvL/qSs6a9ydA+3Zh25h7JEldDjP0oW5W63u5rque9z4HPVfUNd/mvQBtDkIj0Tb1wcnI+hJMPa7m7zheRHFFEpAqnRs9Ib1uq+hP33/9Eab+UCRPpFbXmWBSKEZmWjUL1pceyprxWrMrF9TQMNdiXlDWwQNOocea7ShdQmfIbje27JLAlK4j1NS6CWq+C0LKpnn4jC/ddb6xrhD7xtJ1JXKWPAz95ryyF4b0ltZx+3wz69ajiwXP2on+PZNzzW1maL8tTdFR1mYh8JiI7qur7ONapuWmbzcLxuxJgBPCl+38fYBHONGNeosap/xOodTvTzuNWVS+K2L5RwmT47rYieB26MJFeqci/lYeO4Ku/mBS4n1FIUi24JPXl4+cfYM1CJ0pw2PGn5dy26woJ5pu3omvsIquQ5/KZa6bz0bOL2DbD+A0isoxGwCaQVXc9SN3MOaxLqy0YF6nafx/EECWYSVyl1xoMW3vQYp4Plq/j1Htn0KOqCw+esyeDehcxHUPxuRh40I0I/Bg40/umqm4NICL3AI+p6r/d5SOA4/weJOoU4TBV/Zaq3qyqv0q9IrZZ0oSpseeN/Pvo2UUFrdHVvLEhtvqEQYmzVmI2sjm7NzfWs2ZhDeBGCTbE0JeAWfKDUMhz2VjXyEfPLgKyj18/04VBI2DjImppnGy0bKqnbuYcIFxtwaDkq0UYlulrdshqufLWGqz7oi5U7UG//lcW/3yyagMn//4NysuEB8/di+F9i19mLiO7xeay3QZVrXGnDseq6nGq+mWWTfdKiSt3vyeBffweJ6rA+q+I7BKxjYJQqPp0YWrseSP/tj10REHN6eXdKo3VBIyKyfqEUSmvqKLP1uMB6DlqfChfK1/EJLIKeS4rqivY9tARQO7xm09kFTICNhtxiStwrkn1xLFA29qCcZFe+89EMtFc/lbpx6vuV21rDyaAxWs2cvLv36CxuYUHz9mTrfvHN8YjM2tWsXuQzhIR+YGIjHRf1wG+nUuj1iKcC2yHk8G9HmeOUlV1bOhGAxAkijCfwMrlixMmkjDd98WPT0vzxgZ2Hdo2WiiOSKx0lqzsk9tXJ4wIiDD95e1LnFGEXrJd/+bGesorqnxNE0dK3xHTdGFcPliZxJJfX5t8YzqXD1acUYR+xJWJCMIwPlhRsrinrkvUPFVhaw0G9cEKYsHyG0XopTPVIlyxdhPfvPs1vtjQwEPn7pX8RKJTpsDUqfm3y4OpWoSuQ/v1OHmzFHgF+Imqrvazf1QfrCMi7l+y5PO5CfNQSxdXhcS4hSOCj1FSnO3BsWT5JbAflpeYfLIKWXPQ70M0l09WPitXXFEzcYirbDjXpHDTn6nrMn3NDqFFVpBIwfRxEJflypbJyc0X6+s5+fdvsGJdPX88e8/kiyuAe+4xIrBM4Qqp0AnTo5bKKYlkIlGnB8M4ukch5auQ68YUJNIq5esRdkrCdK3BJEUsJgrDEYYmzrOfcRbGYjGq66e+HryptuKMRItzWjAMDavXU9m3RyxtZxJK+URXPnGV79rUfVFHdb/sPj8Grm3c6YZKjtqNjZx23wwWra5j2pl7MGGrLYrdpU5J9Gq3FqOkorHA8WcZdPW32m0TJNIqtS0QKjKrNRItvdZgSJIUJejFT8qGgmHAmmXiPPsZZ+nRg7miCbPtk43Udt23rGbD8rpWPy+TJE1czT3jdhpXraWify9GT7ukIMeMkscqX5TgX495hLrlG6jesjsnPN7+XhY1ynDpww8A7Bqi6x2W9fVNnPGHGSxYvo57TpvI3tv2K3aXOi0dXvkXyrndBN5oLHAistIjjYJEWnm39bN9Om0i0QzUGixGlKApuq3wt52xJLQRnN9NnOdc4yw1jZcePVi3qi5vNKGfiMP07TYsr2vdHoP3rLjFlZ8ko14aVq+ncdVaABpXraVh9fo4umWM9KjB9GtZ90Uddcs3OP8v30DdF3WB9s9HS30969+tCdn7jsmmxmbOuf9N5nxey29O2o0Ddizg1IsJFi8udg+M0uEFVinhjcYCx4KVKauz30gr77Z+tk+nTSSaz3p7vtvzE9kWYxqDkiDk5zcRQehnnKVHD1b3r84bTeg34tC7Xfctq1u3J0Ch1VwkzXIFUNm3BxX9ewFQ0b9XbNOEpkiPGky/ltX9qqne0jnP1Vt2bzdNmGn/TA7u2SirqqLHmPERPkHHor6pmfP+OIs3Fq7m198cx+FjAjoHJoGERBGKyG9E5PZsL9/txFmLMOtBRe4DJgMrVHWMZ/3FwIVAM/B/qnp1rnbyRREGsV7lmx7yHUkYoR6h1zIAm32wMjkEm/TByldWpeUzCSeuspwL375BK7qy8LIrCxJFCOHGQCaMFgM3FI3pl/Rxmmmc5cvg7senxq/fTboP1p0THowcRRhGXOXK4B6mTE6qDmEmMvlgRYkijBuTPljZBFa2OoQAH/zgireD1LxNchTh1Fc+4o4XPmRgr64M7t2VIb27MbjP5r+De3djSJ+uVFe2vVc1Nrdw4YNv8czc5dz09V341u7mp9MLgggY0CRRowhF5PRc76vq/X7aKZbTyTTgDuCB1AoRORA4FhinqvUiEsm2WariCto7t6fe8wqtIJaofNvmKwxcNlydakx+ySMKOrKDuxFxZcjJ3U9B6HzkE1cQLmrMr1NzaruwTtDNleWRrVVhogfDiiugpMQV5L82ucRV+v7jBizxbcWqH9SYEllGrJrFZt7Stdzy9PuMGdqbLXt2ZWntRuYvW8fKde2n+Ht3q3AEWJ9uDO7dlc+/3MjLC1Zyw9GjS1dcJQi/AiofRRFYqvqKiIxMW30B8AtVrXe3CfJIbyWoz1XSxBVk/kXYWNeYUWgljqhWq4QSNQ9WzkjMmEvm+M3Blk+IF6J4c9Lq1hVaXKUTRlxlO4d+UzRs2tBM1+7loZ3f47iGWwxa16GzuTc2t3DVX2bTu1sl952+O1t03/xdbGhqYfnaTSxes5GltRtZsmYTS2s3snTNJpbUbuKtRV+yflMT1xyxE2fs66tEnsUnIjIA+B4wGmj12VDVg/zsn5CwKQB2ACaJyI3AJuAqVX3T785hnNmDJhcNSphkjZkirNLXxSG08lmxGLgpt09QDpEQOqKtQAWRTZBLXK2Y9gAbamZvjsRMgKDyki9asBDCCvxHFxaKYoqrsFarbFF5fsXV3ZfNZ+aTq5h4RH/Ou9VZF0Ro2dqD4fjdix/x3pK13H3qhDbiCqCySxnD+1bnLG/T3KKUlxkKsCkmd99d7B6k8yDwCHAUcD5wOrDS785JcnLvAvQF9gK+CzwqIu1GjIhMEZGZIjKzoamOjcN7hrJahRVXfq1XYTNhp0dY5Yq6Gtt3SevLBKESTA7clFMwFCJy0DsmGhs2GG8/H/ksVxtqZgNuJGavWuPHHzJgTZtXEPJFpRZKXPmNLvSDdzw01QeLxFs/tLz1lYkki6tsUXlBLFczn3SSHXtrF/rdP2pUYIognz9opGYSmbtkLb954QOOHT+Ew3YO55jeIcQVOJnck0U/Vb0XaFTVl1X1LMCX9QqSJbA+B/6uDjNw5tX7p2+kqlPdIo0Tu1QHj7KJ4sgcp7iCzBFWQeq8mRZc7Uj/rD4sMYWoiecdExWV4fxtjApuD2VVVXQfPw4w9/mjCKp0ckUL+h1HX+n1QesrLH7HuR/a3COqst8jvGIql6hKkWRxBZmj8oJkbu/avZyJRzi33IlH9G9Tu9BPO/miCqMSdHq1FEhNDfapruSGo3cudneKT3ubSrFJfbGXishRIrIrjiHIF0WJIgRwfbCeSEURisj5wBBV/ZGI7AA8D4zQHB0MEjHmJ4lkMcWVl2w+WFFuWEGmE/NFFYYhrA9WsWsRQvZxEcShvaVXbWhxFVVE+cHrgxVEWOXj1bXbB+pHvnEeNIqwxxbDdfyBl+YVT7kIK6zAvygw6cjeWNfIQUMWht4/5YOVTthahGEIEk246JzvlWwtwtue+4D/fW4Bd586IbT1qkORkChCTzuTgenAcOA3QC/gx6r6uJ/9i+KDJSIPAQcA/UXkc5xiivcB94nIuziFuk7PJa6CEDX83oS4CmJVypY/KArpx88luPL6Y4WglB3cIzNwE2UUriZgGILWwfRrrUrfLp/gMm31aKnInWIhH6UmroBI4grIKK7AsWL5EVkmrmGQaMJSxcTUoCVeVPUJ999aILBTYbGiCE/K8tYpJo8Ti9UKAjsp+7VaHTjok83Hdv0fZjbvFOhYQfuUhIjEJEcYbuhTn1Ec+bZepY2VfJ/VtKhq3thgvIh4kKnAdGtIUMFVTAolroLmEcv1fthizl6yWbBS7UcprWNxsFODWZg8uSCHEZFPgHU4OTeb0q1dInK1qt4sIr8B2t3sVdVXHaskRREaw29NOZPiKtuD0Y+4SkVPvXNkXy65bTtuv/RD3vj3agD2PHIhl9y2HVDYh1EcVqxMJLU2IcDHzz/AmoU19Bw1nmHHh6jDmDZWsn1Wk6LKO95S42qloai8oD5WqXG8pzuu87WZBLGVS1SBP6fqIOIqPeouXxRe1CjBfLSNIoznx10UOkq6ht+++CFzl65laoaowU7Nv/5VyKMdqKrZfn3Oc/9GmktOkpO7EUpNXHmjp97492rWrGxoFVepdSlrVlRH4qB9jHuKKim1CTONmebGetYsrAFg3bwaWho2982X9SqD5cr7WbfsscKIkzpkjiY1GZUHwcXVpg3NrePYO4bzHSOOMZ6LukFtX7kwLa7So+7qvqjLGYUXNUowH9miCNMxdbx8JD3BaljmLlnLHS98yLHjh/BVOzXYlqOPLnYPAFDVlNKrU9X7vS+gLte+XjqMwMqXeiHFxoHJEVdf6fUBBw76hD2PdIIS9jyyL30GVLYup9alm+tNP4gKFYqfTiEiDMNSXlFFz1HjAeg5ajxllQH6lmGseD9r0JqQ6fiJGDUVlRd2nHXtXt5mXGebcsp3XNNiK4ig8mJaXEH7qLvqftU5o/CiRgnmI1cUYTqFElmm8KbuWLnSdxoj49ipwTw88UT+bfzRP3W93Vd6/gcFnhGRWRne83Ktz3UZKVoUoQl69hmmO3zjSt/bhw63j0lctTl+mt9D6tdjkAdTlCmWXL5YcU8V5vJLKkQUYTZhvnEgtDTUtxNXYcYKOOMlSA3JFFEEcJSILhPiJpc/TxhO3n5GoPHQdehwHfGdK0Ifz2+epSgO7WF9sOISOUGuWdz+WH6iCUspivDW5xZw63MfMPXUCdZ6lYkCRRGKyFBVXeyW5HsWuFhVX/G8fwRwJPBNnESjKXoBo1V1Dz/9KGkfrJYKfzkzQkUJpohQAicbmR5c6Te0MA+lKP4sY/suySqy4vbHKqblKp/V06S4gmA1JE1YFsOIK5NWI5PiqtCYtFpB9imvoLUco0YJ5iNJ16wjRRO+t6SWO174kOPs1GDRUdXF7t8VIvIYsAfwimeTJTj+V8cAszzr1wGX+z1Oh5kizEYc4ioX+R6KX+n1gS9/lLB4/bVMEtQ/qlj+VKYIXCpp4KasnzmMGDc1bRvE96rQ/k+5yPEdKcg9K5e4Sl3nQiUQ9eLHapXv/mLy/lOMqcL0agOlQENTC1f9ZQ59qiu53k4NZqcAM2oi0l1Eeqb+B74KvNu2Gzob+BPwnzQfrL+r6pd+j9XhBVbSuP3SDzl7/Cxuv/TD2NsO+rDM9lCf/9PH+fSsn7Di9od9tbPi9ocDbV8s/AZEpMhZb9DgZzYlrp65Zjq/n/Qoz1wzvc16r39TMRzL85HtO+Iu7xr38XOJq1V3PcjnF/2IVXc9GHc3QnH3ZfO5eLfXuPuy+aHeTzqf3PR33v3GzdTef3+xuxKI3774IfOWruVnx4+xUYO5mDq1EEfZEnhVRGYDM4D/U9Wn0jdS1WZguIiEvmAdWmDFZb0KOz04sXx+4Mgqv4SJ2vKDt1adn0i/pEQG5sNkoe+WXrXGPrNJy5U3inBi+fzECalMZBvH3vVxks9yVTdzDgB1M+f4tqQUKhouXxSg3yjBoES1Yk3qs6D1lYnU+Wve2EDt9LkA1E6fm9h7SzrvLanlty/aqUFfnHde7IdQ1Y9VdZz72llVb8yx+ULgPyLyQxG5IvXye6wOK7ACT/EYIN/DMWpkVZi2oz5QvbXq/ET6JTkyMEVQy1U+cn3mQmZj95IenZokv5pcZBvH3vXFoqxrFdUTxwLQe9JoX/50hUw1kC8KMEiUYKEIIs7Ku1XSe9JowDn//UYmf6owNTW4RfdKbjjGTg2WIB8BT+BopZ6ely9KOoqw+4DhutNxmcVkJOsVhLJg5RJYXqFjOrLKS6a2gzi8Z3N0b97YwPL1/lWrqezspqMIw2b395O+I9NnDiKwTFmvCjXW4iRbv0/efsbbqrqb33aCRBH6jRhs2VTv++FuWmD59cHKdc3jGhNBIwozfZZsbXgd3b2RuF8u65noKML/fXYBtz3/AfecNpFDR29ZkGOWNAmrRRiVDmnBimy9CuHcHoQ4H3iZ2jYxLRQ0tUCpWq6ijJ0o4soU6de6FMUV5Ox3SxzH8yuugKKJK7/ku+ZJGBNBpxW959J7LwoSaFBovFODVlz55HFfNZQLhogMEJFbROTfIvJC6uV3/w4psPKRbo1oqTc7l58eueVH4ET1hzDlT1HMzO5xEmVaMOt48SHE/frpRLVeJdW/Kuy4jDPSNiimfX2CZtWPmoW/UPgRTZl8rZJ0rU3xyoKVfOfBt+zUYFAmTCh2D9J5EJgPbA38GPgEeNPvzh1OYAW1QKyY9gCffu/7rJj2gLMiz0Mz3/RgtsitXESNLPSzfxIfvknDz9hpN15yMP+nj/P6Mbcx/6e5f5WZEFdJJOy4jjPSNhNBogajJBQFp5bgQwf+kZeve9FXO0G3TzKZBFh6VGOpZYhP570ltZx67xucdt8MWlT57bd3o0+1jRr0zdChxe5BOv1U9V6gUVVfVtWzgIP87tyhBJafB6TXGtFSX8+GmtkAbKiZHdmSlan+W76HX9Tov7iiBzsSpqxX7cZLDsvGlj1WtEZffvHy+7Hl7kmquAo7Lgs9noNEDfq1ZGUTV9lqCWYj6PZJIJtAyrQ+aFRjkmsTLl6zkSserWHyb17lncW1/HDyaJ67Yn/22Lq4gRmWyKS+dEtF5CgR2RXwfVE7lMDKR/pUT1lVFd3HjwOg+/hxlFVF8xsKU/8tU9TUpG6LfB8zSGRiUh/GcWKi+HeKNuMlT4SkN/oyV+3BKNarJF/PsBGzcUbappPP78obNVg9cWxkv8JMtQRNbp9UsomuJEY1BqV2YyM/f3IeB/7yJZ6Ys5Tz9tuWl797IGd/ZWuqupTe57G046ci0hu4ErgK+D0BMrl3mCjCoNYrLy319Y648uFT4yeCMFUrLMgDMBXZkxJX0zeO8L2vd/98+IkozFWXEOKvTeglShRhEMtVtvGTacy09KrN+bD1jpF8tQfDCqwkiysvYSPWckQRGqtFGCRqsKxrla/pQT9WlqD1Ib3bl8oU2vQ1O/jua/q1zhWNmKlszuzJPy14FGF9UzN/fO1T7njxQ2o3NnL8rkO58qs7MrRPt0jtdnqmTDGSbDRqFKGIdAXOB7YD3gHuVdWmoO0UpRahiNwHTAZWqOoYd90twNFAA07uiTNVdY2pY+ZKyxDVcpX+kAzzSzP9YTKp26JAIquQv/5SAqKQQisocYkrCBYhaVpclYqwShF2XMY9noNEDfq93n6nsILeH0rRchVECAa51sWuTdjSojzxzlJueXo+n63eyKTt+3PNETux85DeRetTh6Iwmdz9cD/O9OB04AhgNJA9/08WijVFOA04PG3ds8AYVR0LLACu9duYkaSiEaxXptmwwYlEDzJVmI8gNQpHdf3Ul89QHOfDRMSWCXGVNLL584XxUYri11QMHz/TxwwirlIkOR1A3Jg+/7Urc99b0sVZuu9ZsWoRvvbRFxz3u/9wyUNv06OqggfO2oM/nr2nFVcmSU4U4WhVPUVV7wZOACaFaaQoAktVXwFWp617xmOCex0YZup4eZOKJoRJ3RbxnQvWMGrHFXzngjWt66ISJCorFQXpJ/oNHJFlSmiVRA3DkFGm6QSxXqWuSabafEGj7aJE6BU6uq9Yx7RsxnTtwqv3n8FVX5nB1fvP8LV9ehTly9e9yLvfuJlPbvq7kf744aOV6zlr2pucdM/rrFpXz6+/OY7/u/gr7LfDgIL1odPw1lvF7kGKVlUfZmowRVKd3M8Cnsz0hohMEZGZIjKzadOG6BnbIZbEomGmcjZsaOGJfzl9eeJfm1otWVHIFJWVrW/eKEgIFv0WVWRFqWHoHRONDRsi9SMvK7pGbiKIuPJek2y1+fxG20WJ0Isa3Tep26KMrziO6R0PzRvajocw1is/JDnCLSymaxfWrmzgy2XO/eTLZQ15LVnpUZR1X9S1LtdOnxu7JWtDfRO/eHI+h9/6Cm8uXM01R+zEC1cdwNd2G0ZZmcR6bEvRGScia93XOmBs6n8RWeu3kcQJLBG5DmjCSfDVDlWdqqoTVXViWY/uBetXmPI4QZjUbRHdu5cx+WjnAT756K50717W+l5YgkRleaMgIXf0WyaiWLOi1DD0jomKysKNiUJQUV2Rtzaf32i7KBF6fvcNKqRyvRe2v97xUN5983gIK6466/Sg6Si/3gMq2WKQcz/ZYlAlvQdkv7dM6rOgXRRldb/q1mW/tSDDoKo8PnsJB//qZe56+SOOGz+UF797AOfvvy1dK2xkYKwMHlzsHgCgquWq2st99VTVLp7/e/ltp2hRhCIyEngi5eTurjsDOA84WFXr8rXRbfBw3fqs7HXGTFqvggqsoBYs74Nmw4aWVnGVImhUYTrpkTq5oglTPg/zNm0V+nhhHeDT6/mFiSLc4RtX+j5eLguon/qD6eQTmEEFeWocZYuqCxOlF6UWXaZ9TUxj5xrf3mOGjSKMYrkyFT1ogmJEEZquXVi7siGnuEqRiiZMj7pMLaec3U1GEb6/bB3XP/4ur3+8mjFDe/GTY8ew24gt/DZtSQi2FmEaInI4cDVwjB9xZYQEiiugnbgyQZAbZEV1ReTIpbDWrCTWMCwW3nGU7fqFefBFeVjGIa7ytRP14R63uOromI7o9COuYLOYTL8XxRFVuXZTIz/511yOvH0685et48bjx/DPC79ixVWhueGGYvfAKEURWCLyEPAasKOIfC4iZwN3AD2BZ0WkRkTuCtt+S0O9Mcf2TCIh39y/X3+YID4NfvxVghzfjwj0a23JdT4MOMAn5kcARKtbmW/cRM3UnX7dc42DqP406VN/QfwFU9um72MyajaFVgS7D4SJYi10VFvQa5fP18n08aKSfrz070Vq2YTVsKVF+duszznoly/zh/8u5MTdh/PilQdw8p5bUW79rArPj39csEOJSLmIvC0iT8R1jKLkwVLVkzKsvtdE258/9gDr5tXQffw4Bp5xWvYNQzq2z//p43zx8vv0239Hxv66vQXymWumc+ezi9jzyL5cctt2Gdu4/dIPW513Jx/dlUl39gnVl/T2Uv4qqf+zHd8k3vOx0w+OybjNkAFrQk0ZutGEuwbdr8eyJt+pGrqt8J+qYcW0B9hQM5vue+3CwEtODNSn1HlaeegIvvqL9hG/z1wznY+eXcS27vtBraDeMXDJbdu1W861bVDShdB3LljDE//axOSju/K7PGM5te3gIWUsXdLSbp+g+d9MsuquB6mbOYfqiWPpf/7JvqxXn9z0d2qnz2XdIVuz/40Hxt7Huy+bz8wnVzHxiP6cd+tOebe/ev8ZfLmsgS0GVXLzy3vEfryopB9v7g2PM/PJVWzlnt+Xr3uRT59b2LochXcX13L94+8x69Mv2XVEH6aduTtjhtqUC52IS4F5gG+fqqAkyjoQlZaGetbNqwHM1BZMt740b2xoU18u0y+rTBFfXryRURA9WjA90sp0HbdcVqz082HSkuWNKkwCbeoQBoxy9J6nVI1KL+k1LCeWBwuJTx8Da1Y2ZB0HUaIBMzmsB4l89W67dElL1n3isGTlI0zdweaNDdROnwsUplZgY11joKi+TFF7h1R/6PtlOoowH+nHq13Z0LqcHkWYOt9hrFjNLcoP/vEOx9zxKp+s2sAtJ4zlb+fvY8VVJ0JEhgFH4ZS+iY0OJbDKKqvoOWo8kKe2YEjrlbe+XKZag9kivrx4I6OgbbRgPjI9eNIjrQpVxw3819tLEURkeaMKk0CQOoTp+Bk33hqWQa9b+hjoM6Ay6zgIE5mXKxIwW+Rrvm0HDynLuU+hRVaYuoPl3SrpPWk0UJhagRXVFYGi+oJE7WVi8oCFHDW5q+/jRSU9arH3gMrW5fQowqDn25u6Y/7SWh6a8Rmn7zOSF646gG9MHG7TLiSFiCWMPPRPXW/3NSXt/VtxfL6j50LKQUnXIswURbhpoG6uLZiJiI7t4Pxy3XXoqozvfaXXB76ibnZr+QQI7tCebfrEe8wgUT8mahPmq7eXTpDpwoXfvu5tVd3N7/apWoSx1SGsr6dsePbvTK6AiHw16BrrGjlw0Ce5upoT73Wf1G1Rm2jU9HHjd4z4FTqZIl/zbetnn/R+B40irBo5TAf98BK/mweqO5hidPdPC1bOZlKfBYGj+lJRe4dUh0vWmn6dnquL1/Ug/fNt2tDMm42jWpfTv0cP7HlfoDHRd6tR+p/X32DU4NhmhixhmTXLSDb3XFGEIjIZOFJVvyMiBwBXqerkyAfNQIeyYKWIWlswn6Ulm7hK4efm1717mdFowXQrhUnyObsHzUcTcLow1l8YQYkytvI9hKM+pL3iCtqK9/S8VPnGiJ9EoF6CjOXUtn72KYYlK2jkYKFrBQb9fge1XKWTfp1SU4gm8baX/vm6di9vk54i6vneZkB3K66SysSCZFbYFzhGRD4BHgYOEpE/xXGgDiWw8kYOxpCxPUW2WnGZ8PPQ8PqleP9P7RvVH2JNxMiizkDXFWanDfL56OSqNeg3StSvIMmVDDSqqAnjU5hrH5Miy0Sty6QRt2/UyhVtK4WkrlUUoeX19dpbF7Sus1jiRlWvVdVhqjoSOBF4QVVPieNYHUpgmSBMWoFsteLC4q1HmF6bEKLXZ7t4vxou3KeGi/er8b2PqYz1KeIunN1jmf/yUd1WBGw8RLmc1Bh55prpGd/PJK5S1/ni/Wpar3e2ax/U4pQJE21kGq8m9jEhslbd9SCfX/QjVt2VsUgEEDzvVbFL5JiuFZjOPnusYI8Jq9hnD+dLctEFaxiz0wou8lwrr1jKRroDfYpM7WWjGElWLZYolLQPloisBD7N8nZ/IPdcXjCytVdG21QCb+NvWstve17edv9mOp7fz9sFGOdZno1Tmshv/8IStr2tVNV3VdWEjIl0/I4Rb3u5xkG+doL2zy/52gv6XeiPU/Q9yD5hx0PY72kmkvLdyPaZTPUv/V7xDuCNPAl7DlP9M3VNTN4jLB2DQGMiLkpaYOVCRGaaTJVv20tWe0nog20vWe0ViqSfh87WnsWSVOwUocVisVgsFothrMCyWCwWi8ViMUxHFlhTbXsdur0wJP0z2fZKg6Sfh87WnsWSSDqsD5bFYrFYLBZLsejIFiyLxWKxWCyWomAFlsVisVgsFothrMDKg4iI929noSN93s56DS2dD5Nj3H5vLJZodEiBJSLdDDaXKgXcxW070jkTkQkisk3kXrVtc7iIVIpId3c5ah/3BPYx0jnM9y8Epq+h6fNd7PNj8YHh+0riv7eUwL3PYkkyHe5GLiKHAReJSPB6Ju3bmgz8Q0SmAj8WkZGq2hL2RuP27RGgh2ddpF+HInIU8CRwB/AHEdnRQB/vB4wUbjTdvxDHN30NTZ/v2M6P+/COVuk3xvbcNqUULCUm7ytue0n/3ib+3mexJB5V7TAv4Aic0i8HZHhPAra1LfAxcAAwCbgOeAPY3n2/LGB7BwIfAAe6y93cv13CtOfuMxR41+3jlsBVwFJg55B93MvdP9XHHml99d0eIMBwnPIaRvoX4vyYvobGznfc5wf4OvBX4BngKGCLiOfSaHtum8cCf3Bfk+IcCxH7afK+Yvy6A18BFpv43rrbJ/7eZ1/2VQqvLnQQRGQ08Dvg56r6koj0w6l5Vamq76iqioioqt+8FF8Az7htCfAqTs2+P4rIt1Q1aC2rr+LcpF8XkRHAD0VkI1AnIr9V1c+CNOZOV6wCpgMLgBWq+ksRaQSeEZEDVdV3dVQRGYlzo34JWCUiWwE/F5G1QD8R+b6qfuD3HLrnewnwGs7NNVL/QmLsGpo+33GeHxHZAfgpcBYwEjgP2E5EnlDVj4rdntvmOOAm4ApgBHCniPwMeFxV14dpMw5M31diuu5jgf8AX0T93rqYvvcdDswEZpi491kspUJHmiLshmNybxGRw3HM0T8Bfi0ivwHn5pavERHZWUT2x/lluZuIXKUuwC3A/wGniki5HxO3iEwSkWOBG3B+Fd6GYwWYi3NTrAe+LyJVfk3mbnu/BIYAfYEzU59NVW9zj/F9Eenqs4+HAfcCLwAvA5e7fXsduA94C7hDRHr6PIfbi8juQDWwBXBClP4FJYZraPp8x31+tgCWq+prqvoQ8HNgDHCUiPQM0V5fw+0BDAbmq+q/VfUuHCvOqcDRkCg/NCP3FQAR2c697n2A3sDJEcfRdq4A/DvwX+ACon1vTX9vxojIbsAfgeXAzUS891ksJUWxTWhRX8AOnv/3Bf4X+Ag4n83m+OfwMQWBMxUwB3gcR3AcBLwHXOTZ5jDgdz7aKsPxN3gPx+JxIlAJ/Bq41LPdfsA9AT7v/sB84DB3eQROZfgrPNuMBO7Gx/QFjmWtBlgE3OiuuwQ417PNMJwbdqWP9o7DsdQ9BvwI+BVQC3wnTP9CjAdj1zCm812Q8wM8AHyTzdMw++AIha8GaKPaM5b/ELW9tLb7u33cE3eKyL12c4F9TI+LEP0zdl9x25jsjsuXcfyujgE+Aa4NOY5S7b0C3OP28WJgimebIN9b09+bI3CmQf8F/AnYFUdAXujZJtC9z77sq9ReRe9ApM47N5k64GHPuj2A49O2mwbslaetA3CE0B7u8r+A3YHd3BvrJe6N9QzgeaCnzxvh1cCVwIPAWe66Ks/7pwL/xhFjftq7ArjK/X8EMNptYx3wHWBHt48zyeMnAxwCfAjsjCP+ngMmABVAV892J+NMHfbJ014/nIfuaHd5CvBPnF+wte552N5v/0KMB+PX0PD5ju38uJ/xK8Ce7vK5OGL+AKDCXXc68CiuSMrT3mHAd3EsOGXARThWvFDtudsf5PbrHHf5ZziWkZFAubvuYuDXJsdFiHFk7L7ibrcPMA/Y1V2eijPlOgTnh80PgO0CjKP09u4CfuP+7723+P3eGv3eZGjvcbe9Klz/K3d9oHuffdlXqb1K1gdLnNDmi4DLgH1E5M+q+m1VnSGecGoR+TqOgFiap8nlwHnu/oNwhMYPcZyaHwVOwpkWmQR8U1XX+exqE86D+T7gXBHZGWgArhWRy4DTgFPVv99JE44YAngYWIJzE3wHxxq1I84N+ExV/TJPW+XAaar6noj0wblp76mqs0SkCUBEzgUuxJnOWOOjbz2AQcBcVZ0qIkfinMP5OOJkJ2C8z/4FJY5raPJ8x3J+xIn4+h+3T9Ui8hhORNkPcBzSh+OIOMWJMss5XSQiRwC/wLG0bnTXTcMRgMcGbc/T5i04ouRY1w/pJuBWnAf4Yzj+bYrzIC4KMdxXUtykqm+7/18HTFPVJSJyAM51ugLHmneGz+vube+HwL0iUqmq9W7/zsY5r9/28b01/b1Jb293t70lOD6of8L5cXEOwe59FktpUWyFF+WF8wuwB850w1+BB9PePx14ExgTsN3rgB+4/58D3I5jWegK9A/Y1rbANe7/V+L8Mv6tu/yHEH3bBXgf52F/prtuBxy/mGPd5UCWITZP0RwOLAN2cZe74/hNjArQ1vk4UwKnAjfiWO4uBH7p2aZPAcaGkWto+nybPj84Uy9zgHHu8jeA293/e+I8yKbhWDLewbV65GhvNLAQd6oJx+o2GhjpLp/i9t9Xe55x9DRwlLt8kfv5t8WxjFznnoencKalxsU9PvL01+h9BeeHTC/P/8OAt4HB7rqtcHJN9Y7Y3gB33TY41sudivW9ydLeGe73aHt3TAa699mXfZXaq+gdMPZBnAfB34A/ucujcKZwtjHQ9lPAhJD7DsERUufiRA39CMdZ9JuEDE/GcQReCPzEs+5eHGsURPPd+QlwLZunbIKGZPfGmZq4D89UD85UQJ8ijo8o19DY+TZ9fnCsZ+d7lrcDZrBZEKUKuo8HBvpobwJO1Nw5OIL7ORzH7hdwrCap7Xbx0567bXf3gXqU249P3DZnAPe52/TC8ckZVqwxkqXvRu8rOEKqB/C8u3wKjt9VN4Pt3YQrwAx8/tDfmxztRb4n25d9lcKr6B0w+mGcX5x/wLE4LMD9hRiwDUlb/jowCxgUoV8/wfG1ONpdPggYHqG9LjhTix8DZ7uvmcC2Bs7h13HCsn351eRop8zz/2k4UU7dCzQOjF7DOM531PNDWyfslOWiHCcy8V9stnDsEKK9bE7dzwP7hezjZcBfXFF1s2f9TOCkQoyLCNcq8n0lQ5vTcKygs3AtxgbbGxuyDdPfm2ztRT5/9mVfpfAqegeMfyAnxcCyqDctHD+Qs3GmLCKZst2H0wTPspHEejhOqD/DiUSLfJP2tPsorgXEQFtn4USGGetfMa5hXOc7zPkhsxN2apq3DMeRvhfONNzj5HeaNurUnaPNahwxeYhn3c04aSoKOjZCXCdT9xXB8en7COdH1/ZJas9t0/T3xmh79mVfpfJKTR90CERkCxxxcKWqzonYVgVwKPCRqr5vqH9Bkv0VnDj65yY+rFDVD0226/PYxq+haYKeH9cJ+284uY/2wbE0nuK+V44jsP6ME5U4Hmcac26A9ipV9dvue910s5P714FrcMRQzkSTedo8Hcch/wS3fxcA39L4k86GxuR9xdPmGcCbqvpe0toz/b0phe+hxRIHHUpgAYhIV1U1Uo/LYkkiIjIEWIvjeHwXsCklstz3/4HjiH+8nwdahvbqVfVkz/un4zimn6mq74bsY2ubIvJDHF+mXjgBIL7aLCam7yumf8wk/cebxdIZ6XACy2LpTIhTumUqsFFVTxGR7YEzcZyys1quArQ3CqeW3FOq+nHEPjaq6okisg2OuJqrqg1h2rRYLJakYwWWxVLiiEh/nBxT+7qrJqnqcgPt7YPj47O/qvrN9+Snj4JT+PfzKG1aLBZLkklKvS+LxRISVV2FkwurF/D1KOIqrb3ebnuRxFWGPn7NiiuLxdLRsQLLYilxXCfsI3HqAr6TtPbiatNisViSjJ0itFg6ADE4YRsPFrEBKMVDREbiFNH+c7H7YrF0FqwFq8QQkW+IyHsi0iIiE4vdH0syMC1c4hBCVlwVlZHAt4PsICIlW6vWYkkCVmCVHu8CXwNeKXZHLBaLOURkpIjME5F73B9Rz3gLTKdtu62IPCUis0Rkuojs5K6fJiK3i8h/ReRjETnB3eUXwCQRqRGRy0WkXERuEZE3RWSOiJzn7n+A297jOAlwLRZLSKzASijZbraqOs8m67NYOizb4xSD3xlYg1NeJhNTgYtVdQJwFU79yBSDga/gZNP/hbvuGmC6qo5X1f/Fyaxeq6q7A7sD54rI1u62uwGXquoO5j6WxdL5sCbgZLM9Tp22c0XkUZyb7Z+K3CeLxRIfC1W1xv1/Fs7UXhtEpAdOCo2/iEhqdZVnk3+oagswV0S2zHKcrwJjPRau3jj3mwZghqoujPIhLBaLFVhJJ+/N1mIRkVuAo3Eejh/hZFxfU9ROWcJS7/m/Gcg0RVgGrFHV8T7akCzbCI4F7Ok2K0UOADb46ajFYsmNnSJMNuk3WyuILZl4FqeI7lhgAXBtkftjiRFVXQssFJFvgFMmR0TG5dltHdDTs/w0cIFbJxAR2cGtIWmxWAxhBZbFUiLk8Mt7RlWb3M1eB4YVs5+WgnAycLaIzAbeA47Ns/0coFlEZovI5cDvcZzY3xKRd4G7sT/gLBaj2DxYCcXNW/OEqo5xl68CegCzgd8AA3CcYGtU9bAiddNSQNwx8SEwUVVrXL+8x1X1T55t/gU84l1nsVgslsJjBZbFUiK4AutZVd3eXf4eUKGqP3WXrwMm4pSisV9si8ViKSLWJGyxlBYZnaBF5AycsPyDrbjqOIjIb9lcxDvFbar6h2L0x2Kx+McKLIulxBGRw4Grgf1Vta7Y/bGYQ1UvLHYfLBZLOKyTu8VS+tyBEyH2rJup+65id8hisVg6O9YHy2KxWCwWi8Uw1oJlsVgsFovFYhgrsCwWi8VisVgMYwWWxWKxWCwWi2GswLJYLBaLxWIxjBVYFovFYrFYLIaxAstisVgsFovFMFZgWSwWi8VisRjGCiyLxWKxWCwWw/w/k855TRv0SWoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skopt.plots import plot_objective\n",
    "\n",
    "_ = plot_objective(optimize_result, n_points=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skopt.plots import plot_evaluations\n",
    "\n",
    "_ = plot_evaluations(optimize_result, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn more by exploring further\n",
    "[examples](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#tutorials)\n",
    "or find more framework options in the\n",
    "[full API reference](https://kernc.github.io/backtesting.py/doc/backtesting/index.html#header-submodules)."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "encoding": "# -*- coding: utf-8 -*-"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
